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  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted alphabetically
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 61)
Annals of Applied Statistics     Full-text available via subscription   (Followers: 39)
Applied Categorical Structures     Hybrid Journal   (Followers: 4)
Argumentation et analyse du discours     Open Access   (Followers: 10)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 8)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 4)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 13)
Bernoulli     Full-text available via subscription   (Followers: 9)
Biometrical Journal     Hybrid Journal   (Followers: 10)
Biometrics     Hybrid Journal   (Followers: 51)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 18)
Building Simulation     Hybrid Journal   (Followers: 1)
Bulletin of Statistics     Full-text available via subscription   (Followers: 4)
CHANCE     Hybrid Journal   (Followers: 5)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 11)
Computational Statistics     Hybrid Journal   (Followers: 14)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 37)
Current Research in Biostatistics     Open Access   (Followers: 8)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 11)
Demographic Research     Open Access   (Followers: 16)
Electronic Journal of Statistics     Open Access   (Followers: 8)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
ESAIM: Probability and Statistics     Full-text available via subscription   (Followers: 5)
Extremes     Hybrid Journal   (Followers: 2)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 8)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 13)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 5)
Handbook of Statistics     Full-text available via subscription   (Followers: 7)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
International Journal of Quality, Statistics, and Reliability     Open Access   (Followers: 17)
International Journal of Stochastic Analysis     Open Access   (Followers: 3)
International Statistical Review     Hybrid Journal   (Followers: 12)
International Trade by Commodity Statistics - Statistiques du commerce international par produit     Full-text available via subscription  
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 4)
Journal of Applied Statistics     Hybrid Journal   (Followers: 20)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 20)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 39, SJR: 3.664, CiteScore: 2)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 20)
Journal of Econometrics     Hybrid Journal   (Followers: 82)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 6)
Journal of Forecasting     Hybrid Journal   (Followers: 17)
Journal of Global Optimization     Hybrid Journal   (Followers: 7)
Journal of Interactive Marketing     Hybrid Journal   (Followers: 10)
Journal of Mathematics and Statistics     Open Access   (Followers: 8)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 6)
Journal of Probability and Statistics     Open Access   (Followers: 10)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 32)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 5)
Journal of Statistical Physics     Hybrid Journal   (Followers: 13)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 8)
Journal of Statistical Software     Open Access   (Followers: 20, SJR: 13.802, CiteScore: 16)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 72, SJR: 3.746, CiteScore: 2)
Journal of the Korean Statistical Society     Hybrid Journal   (Followers: 1)
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 31)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 26)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 43)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 16)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 30)
Law, Probability and Risk     Hybrid Journal   (Followers: 8)
Lifetime Data Analysis     Hybrid Journal   (Followers: 7)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Metrika     Hybrid Journal   (Followers: 4)
Modelling of Mechanical Systems     Full-text available via subscription   (Followers: 1)
Monte Carlo Methods and Applications     Hybrid Journal   (Followers: 6)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 2)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 5)
Optimization Letters     Hybrid Journal   (Followers: 2)
Optimization Methods and Software     Hybrid Journal   (Followers: 8)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 34)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 17)
Probability Surveys     Open Access   (Followers: 4)
Queueing Systems     Hybrid Journal   (Followers: 7)
Research Synthesis Methods     Hybrid Journal   (Followers: 7)
Review of Economics and Statistics     Hybrid Journal   (Followers: 124)
Review of Socionetwork Strategies     Hybrid Journal  
Risk Management     Hybrid Journal   (Followers: 15)
Sankhya A     Hybrid Journal   (Followers: 2)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Sequential Analysis: Design Methods and Applications     Hybrid Journal  
Significance     Hybrid Journal   (Followers: 7)
Sociological Methods & Research     Hybrid Journal   (Followers: 37)
SourceOCDE Comptes nationaux et Statistiques retrospectives     Full-text available via subscription  
SourceOCDE Statistiques : Sources et methodes     Full-text available via subscription  
SourceOECD Bank Profitability Statistics - SourceOCDE Rentabilite des banques     Full-text available via subscription   (Followers: 1)
SourceOECD Insurance Statistics - SourceOCDE Statistiques d'assurance     Full-text available via subscription   (Followers: 2)
SourceOECD Main Economic Indicators - SourceOCDE Principaux indicateurs economiques     Full-text available via subscription   (Followers: 1)
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
SourceOECD Monthly Statistics of International Trade     Full-text available via subscription   (Followers: 1)
SourceOECD National Accounts & Historical Statistics     Full-text available via subscription  
SourceOECD OECD Economic Outlook Database - SourceOCDE Statistiques des Perspectives economiques de l'OCDE     Full-text available via subscription   (Followers: 2)
SourceOECD Science and Technology Statistics - SourceOCDE Base de donnees des sciences et de la technologie     Full-text available via subscription  
SourceOECD Statistics Sources & Methods     Full-text available via subscription   (Followers: 1)
SourceOECD Taxing Wages Statistics - SourceOCDE Statistiques des impots sur les salaires     Full-text available via subscription  
Stata Journal     Full-text available via subscription   (Followers: 9)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Statistical Applications in Genetics and Molecular Biology     Hybrid Journal   (Followers: 5)
Statistical Communications in Infectious Diseases     Hybrid Journal  
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Statistical Methodology     Hybrid Journal   (Followers: 7)
Statistical Methods and Applications     Hybrid Journal   (Followers: 6)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 27)
Statistical Modelling     Hybrid Journal   (Followers: 19)
Statistical Papers     Hybrid Journal   (Followers: 4)
Statistical Science     Full-text available via subscription   (Followers: 13)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Statistics & Risk Modeling     Hybrid Journal   (Followers: 2)
Statistics and Computing     Hybrid Journal   (Followers: 13)
Statistics and Economics     Open Access   (Followers: 1)
Statistics in Medicine     Hybrid Journal   (Followers: 193)
Statistics, Politics and Policy     Hybrid Journal   (Followers: 6)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 14)
Stochastic Models     Hybrid Journal   (Followers: 3)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
Teaching Statistics     Hybrid Journal   (Followers: 7)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
TEST     Hybrid Journal   (Followers: 3)
The American Statistician     Full-text available via subscription   (Followers: 24)
The Annals of Applied Probability     Full-text available via subscription   (Followers: 8)
The Annals of Probability     Full-text available via subscription   (Followers: 10)
The Annals of Statistics     Full-text available via subscription   (Followers: 34)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 11)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)

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Similar Journals
Journal Cover
Statistical Methodology
Journal Prestige (SJR): 0.378
Citation Impact (citeScore): 1
Number of Followers: 7  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1572-3127
Published by Elsevier Homepage  [3203 journals]
  • HeartCast: Predicting acute hypotensive episodes in intensive care units
    • Authors: Sun-Hee Kim; Lei Li; Christos Faloutsos; Hyung-Jeong Yang; Seong-Whan Lee
      Pages: 1 - 13
      Abstract: Publication date: Available online 17 July 2016
      Source:Statistical Methodology
      Author(s): Sun-Hee Kim, Lei Li, Christos Faloutsos, Hyung-Jeong Yang, Seong-Whan Lee
      Acute hypotensive episodes (AHEs) are serious clinical events in intensive care units (ICUs), and require immediate treatment to prevent patient injury. Reducing the risks associated with an AHE requires effective and efficient mining of data generated from multiple physiological time series. We propose HeartCast, a model that extracts essential features from such data to effectively predict AHE. HeartCast combines a non-linear support vector machine with best-feature extraction via analysis of the baseline threshold, quartile parameters, and window size of the physiological signals. Our approach has the following benefits: (a) it extracts the most relevant features; (b) it provides the best results for identification of an AHE event; (c) it is fast and scales with linear complexity over the length of the window; and (d) it can manage missing values and noise/outliers by using a best-feature extraction method. We performed experiments on data continuously captured from physiological time series of ICU patients (roughly 3 GB of processed data). HeartCast was found to outperform other state-of-the-art methods found in the literature with a 13.7% improvement in classification accuracy.

      PubDate: 2016-07-23T08:49:15Z
      DOI: 10.1016/j.stamet.2016.07.001
      Issue No: Vol. 33 (2016)
       
  • Edge density of new graph types based on a random digraph family
    • Authors: Elvan Ceyhan
      Pages: 31 - 54
      Abstract: Publication date: Available online 27 July 2016
      Source:Statistical Methodology
      Author(s): Elvan Ceyhan
      We consider two types of graphs based on a family of proximity catch digraphs (PCDs) and study their edge density. In particular, the PCDs we use are a parameterized digraph family called proportional-edge (PE) PCDs and the two associated graph types are the “underlying graphs” and the newly introduced “reflexivity graphs” based on the PE-PCDs. These graphs are extensions of random geometric graphs where distance is replaced with a dissimilarity measure and the threshold is not fixed but depends on the location of the points. PCDs and the associated graphs are constructed based on data points from two classes, say X and Y , where one class (say class X ) forms the vertices of the PCD and the Delaunay tessellation of the other class (i.e., class Y ) yields the (Delaunay) cells which serve as the support of class X points. We demonstrate that edge density of these graphs is a U -statistic, hence obtain the asymptotic normality of it for data from any distribution that satisfies mild regulatory conditions. The rate of convergence to asymptotic normality is sharper for the edge density of the reflexivity and underlying graphs compared to the arc density of the PE-PCDs. For uniform data in Euclidean plane where Delaunay cells are triangles, we demonstrate that the distribution of the edge density is geometry invariant (i.e., independent of the shape of the triangular support). We compute the explicit forms of the asymptotic normal distribution for uniform data in one Delaunay triangle in the Euclidean plane utilizing this geometry invariance property. We also provide various versions of edge density in the multiple triangle case. The approach presented here can also be extended for application to data in higher dimensions.

      PubDate: 2016-07-28T08:58:50Z
      DOI: 10.1016/j.stamet.2016.07.003
      Issue No: Vol. 33 (2016)
       
  • Some new results on the Rényi quantile entropy Ordering
    • Authors: Lei Yan; Dian-tong Kang
      Pages: 55 - 70
      Abstract: Publication date: Available online 25 May 2016
      Source:Statistical Methodology
      Author(s): Lei Yan, Dian-tong Kang
      Rényi (1961) proposed the Rényi entropy. Ebrahimi and Pellerey (1995) and Ebrahimi (1996) proposed the residual entropy. Recently, Nanda et al. (2014) obtained a quantile version of the Rényi residual entropy, the Rényi residual quantile entropy (RRQE). Base on the RRQE function, they defined a new stochastic order, the Rényi quantile entropy (RQE) order, and studied some properties of this order. In this paper, we focus on further properties of this new order. Some characterizations of the RQE order are investigated, closure and reversed closure properties are obtained, meanwhile, some illustrative examples are shown. As applications of a main result, the preservation of the RQE order in several stochastic models are discussed.

      PubDate: 2016-05-30T14:46:14Z
      DOI: 10.1016/j.stamet.2016.04.003
      Issue No: Vol. 33 (2016)
       
  • Symmetric directional false discovery rate control
    • Authors: Sarah E. Holte; Eva K. Lee; Yajun Mei
      Pages: 71 - 82
      Abstract: Publication date: Available online 24 August 2016
      Source:Statistical Methodology
      Author(s): Sarah E. Holte, Eva K. Lee, Yajun Mei
      This research is motivated from the analysis of a real gene expression data that aims to identify a subset of “interesting” or “significant” genes for further studies. When we blindly applied the standard false discovery rate (FDR) methods, our biology collaborators were suspicious or confused, as the selected list of significant genes was highly unbalanced: there were ten times more under-expressed genes than the over-expressed genes. Their concerns led us to realize that the observed two-sample t -statistics were highly skewed and asymmetric, and thus the standard FDR methods might be inappropriate. To tackle this case, we propose a symmetric directional FDR control method that categorizes the genes into “over-expressed” and “under-expressed” genes, pairs “over-expressed” and “under-expressed” genes, defines the p -values for gene pairs via column permutations, and then applies the standard FDR method to select “significant” gene pairs instead of “significant” individual genes. We compare our proposed symmetric directional FDR method with the standard FDR method by applying them to simulated data and several well-known real data sets.

      PubDate: 2016-08-26T10:25:34Z
      DOI: 10.1016/j.stamet.2016.08.002
      Issue No: Vol. 33 (2016)
       
  • Estimation and goodness-of-fit in latent trait models: A comparison among
           theoretical approaches
    • Authors: Juan Carlos Bustamante; Edixon Chacón
      Pages: 83 - 95
      Abstract: Publication date: Available online 26 May 2016
      Source:Statistical Methodology
      Author(s): Juan Carlos Bustamante, Edixon Chacón
      Two theoretical approaches are usually employed for the fitting of ordinal data: the underlying variables approach (UV) and the item response theory (IRT). In the UV approach, limited information methods [generalized least squares (GLS) and weighted least squares (WLS)] are employed. In the IRT approach, fitting is carried out with full information methods [Proportional Odds Model (POM), and the Normal Ogive (NOR)]. The four estimation methods (GLS, WLS, POM and NOR) are compared in this article at the same time, using a simulation study and analyzing the goodness-of-fit indices obtained. The parameters used in the Monte Carlo simulation arise from the application of a political action scale whose two-factor structure is well known. The results show that the estimation method employed affects the goodness-of-fit to the model. In our case, the IRT approach shows a better fitting than UV, especially with the POM method.

      PubDate: 2016-05-30T14:46:14Z
      DOI: 10.1016/j.stamet.2016.05.002
      Issue No: Vol. 33 (2016)
       
  • Change detection for uncertain autoregressive dynamic models through
           nonparametric estimation
    • Authors: Nadine Hilgert; Ghislain Verdier; Jean-Pierre Vila
      Pages: 96 - 113
      Abstract: Publication date: Available online 1 September 2016
      Source:Statistical Methodology
      Author(s): Nadine Hilgert, Ghislain Verdier, Jean-Pierre Vila
      A new statistical approach for on-line change detection in uncertain dynamic system is proposed. In change detection problem, the distribution of a sequence of observations can change at some unknown instant. The goal is to detect this change, for example a parameter change, as quickly as possible with a minimal risk of false detection. In this paper, the observations come from an uncertain system modeled by an autoregressive model containing an unknown functional component. The popular Page’s CUSUM rule is not applicable anymore since it requires the full knowledge of the model. A new detection CUSUM-like scheme is proposed, which is based on the nonparametric estimation of the unknown component from a learning sample. Moreover, the estimation procedure can be updated on line which ensures a better detection, especially at the beginning of the monitoring procedure. Simulation trials were performed on a model describing a water treatment process and show the interest of this new procedure with respect to the classic CUSUM rule.

      PubDate: 2016-09-04T10:44:07Z
      DOI: 10.1016/j.stamet.2016.08.003
      Issue No: Vol. 33 (2016)
       
  • Large sample convergence diagnostics for likelihood based inference:
           Logistic regression
    • Authors: Michael Brimacombe
      Pages: 114 - 130
      Abstract: Publication date: Available online 24 August 2016
      Source:Statistical Methodology
      Author(s): Michael Brimacombe
      A general diagnostic approach to the evaluation of asymptotic approximation in likelihood based models is developed and applied to logistic regression. The expected asymptotic and observed log-likelihood functions are compared using a chi distribution in a directional Bayesian setting. This provides a general approach to assessing and visualizing non-convergence in higher dimensional models. Several well-known examples from the logistic regression literature are discussed.

      PubDate: 2016-08-26T10:25:34Z
      DOI: 10.1016/j.stamet.2016.08.001
      Issue No: Vol. 33 (2016)
       
  • Nonlinear regression models under skew scale mixtures of normal
           distributions
    • Authors: Clécio S. Ferreira; Víctor H. Lachos
      Pages: 131 - 146
      Abstract: Publication date: Available online 8 September 2016
      Source:Statistical Methodology
      Author(s): Clécio S. Ferreira, Víctor H. Lachos
      Normal nonlinear regression models are applied in some areas of the sciences and engineering to explain or describe the phenomena under study. However, it is well known that several phenomena are not always represented by the normal model due to lack of symmetry or the presence of heavy-and-lightly tailed distributions related to the normal law in the data. This paper proposes an extension of nonlinear regression models using the skew-scale mixtures of normal (SSMN) distributions proposed by Ferreira et al. (2011). This class of models provides a useful generalization of the symmetrical nonlinear regression models since the random term distributions cover both asymmetric and heavy-tailed distributions, such as the skew-t-normal, skew-slash and skew-contaminated normal, among others. An expectation-maximization (EM) algorithm for maximum likelihood (ML) estimates is presented and the observed information matrix is derived analytically. Some simulation studies are presented to examine the performance of the proposed methods, with relation to robustness and asymptotic properties of the ML estimates. Finally, an illustration of the method is presented considering a dataset previously analyzed under normal and skew-normal (SN) nonlinear regression models. The main conclusion is that the ML estimates from the heavy tails SSMN nonlinear models are more robust against outlying observations compared to the corresponding SN estimates.

      PubDate: 2016-09-09T11:11:09Z
      DOI: 10.1016/j.stamet.2016.08.004
      Issue No: Vol. 33 (2016)
       
  • Discrete time software reliability modeling with periodic debugging
           schedule
    • Authors: Sudipta Das; Anup Dewanji; Debasis Sengupta
      Pages: 147 - 159
      Abstract: Publication date: Available online 9 September 2016
      Source:Statistical Methodology
      Author(s): Sudipta Das, Anup Dewanji, Debasis Sengupta
      In many situations, multiple copies of a software are tested in parallel with different test cases as input, and the detected errors from a particular round of testing are debugged together. In this article, we discuss a discrete time model of software reliability for such a scenario of periodic debugging. We propose likelihood based inference of the model parameters, including the initial number of errors, under the assumption that all errors are equally likely to be detected. The proposed method is used to estimate the reliability of the software. We establish asymptotic normality of the estimated model parameters. The performance of the proposed method is evaluated through a simulation study and its use is illustrated through the analysis of a data set obtained from testing of a real-time flight control software. We also consider a more general model, in which different errors have different probabilities of detection.

      PubDate: 2016-09-17T11:24:06Z
      DOI: 10.1016/j.stamet.2016.08.006
      Issue No: Vol. 33 (2016)
       
  • Constructing tests to compare two proportions whose critical regions
           guarantee to be Barnard convex sets
    • Authors: Félix Almendra-Arao; José Juan Castro-Alva; Hortensia Reyes-Cervantes
      Pages: 160 - 171
      Abstract: Publication date: Available online 7 September 2016
      Source:Statistical Methodology
      Author(s): Félix Almendra-Arao, José Juan Castro Alva, Hortensia Reyes-Cervantes
      In both statistical non-inferiority (NI) and superiority (S) tests, the critical region must be a Barnard convex set for two main reasons. One, being computational in nature, based on the fact that calculating test sizes is a computationally intensive problem due to the presence of a nuisance parameter. However, this calculation is considerably reduced when the critical region is a Barnard convex set. The other reason is that in order for the NI/S statistical tests to make sense, its critical regions must be Barnard convex sets. While it is indeed possible for NI/S tests’ critical regions to not be Barnard convex sets, for the reasons stated above, it is desirable that they are. Therefore, it is important to generate, from a given NI/S test, a test which guarantees that the critical regions are Barnard convex sets. We propose a method by which, from a given NI/S test, we construct another NI/S test, ensuring that the critical regions corresponding to the modified test are Barnard convex sets, we illustrate this through examples. This work is theoretical because the type of developments refers to the general framework of NI/S testing for two independent binomial proportions and it is applied because statistical tests that do not ensure that their critical regions are Barnard convex sets may appear in practice, particularly in the clinical trials area.

      PubDate: 2016-09-09T11:11:09Z
      DOI: 10.1016/j.stamet.2016.08.005
      Issue No: Vol. 33 (2016)
       
  • Confidence ellipsoids for the primary regression coefficients in two
           seemingly unrelated regression models
    • Authors: Kent R. Riggs; Phil D. Young; Dean M. Young
      Pages: 1 - 13
      Abstract: Publication date: Available online 3 March 2016
      Source:Statistical Methodology
      Author(s): Kent R. Riggs, Phil D. Young, Dean M. Young
      We derive two new confidence ellipsoids (CEs) and four CE variations for covariate coefficient vectors with nuisance parameters under the seemingly unrelated regression (SUR) model. Unlike most CE approaches for SUR models studied so far, we assume unequal regression coefficients for our two regression models. The two new basic CEs are a CE based on a Wald statistic with nuisance parameters and a CE based on the asymptotic normality of the SUR two-stage unbiased estimator of the primary regression coefficients. We compare the coverage and volume characteristics of the six SUR-based CEs via a Monte Carlo simulation. For the configurations in our simulation, we determine that, except for small sample sizes, a CE based on a two-stage statistic with a Bartlett corrected ( 1 − α ) percentile is generally preferred because it has essentially nominal coverage and relatively small volume. For small sample sizes, the parametric bootstrap CE based on the two-stage estimator attains close-to-nominal coverage and is superior to the competing CEs in terms of volume. Finally, we apply three SUR Wald-type CEs with favorable coverage properties and relatively small volumes to a real data set to demonstrate the gain in precision over the ordinary-least-squares-based CE.

      PubDate: 2016-03-08T16:36:12Z
      DOI: 10.1016/j.stamet.2016.01.004
      Issue No: Vol. 32 (2016)
       
  • On the dynamic cumulative residual quantile entropy ordering
    • Authors: Dian-Tong Kang; Lei Yan
      Pages: 14 - 35
      Abstract: Publication date: Available online 12 February 2016
      Source:Statistical Methodology
      Author(s): Dian-Tong Kang, Lei Yan
      A new stochastic order called dynamic cumulative residual quantile entropy (DCRQE) order is established. Some characterizations of the new order are investigated. Closure and reversed closure properties of the DCRQE order are obtained. Applications of the DCRQE ordering in characterizing the proportional hazard rate model and the k -record values model are considered.

      PubDate: 2016-02-14T15:46:18Z
      DOI: 10.1016/j.stamet.2016.01.008
      Issue No: Vol. 32 (2016)
       
  • Bayesian optimal cluster designs
    • Authors: Satya Prakash Singh; Siuli Mukhopadhyay
      Pages: 36 - 52
      Abstract: Publication date: Available online 11 March 2016
      Source:Statistical Methodology
      Author(s): Satya Prakash Singh, Siuli Mukhopadhyay
      Designing cluster trials depends on the knowledge of the intracluster correlation coefficient. To overcome the issue of parameter dependence, Bayesian designs are proposed for two level models with and without covariates. These designs minimize the variance of the treatment contrast under certain cost constraints. A pseudo Bayesian design approach is advocated that integrates and averages the objective function over a prior distribution of the intracluster correlation coefficient. Theoretical results on the Bayesian criterion are noted when the intracluster correlation follows a uniform distribution. Two data sets based on educational surveys conducted in schools are used to illustrate the proposed methodology.

      PubDate: 2016-03-12T18:34:42Z
      DOI: 10.1016/j.stamet.2016.02.002
      Issue No: Vol. 32 (2016)
       
  • Notes on estimation in Poisson frequency data under an incomplete block
           crossover design
    • Authors: Kung-Jong Lui
      Pages: 53 - 62
      Abstract: Publication date: Available online 18 February 2016
      Source:Statistical Methodology
      Author(s): Kung-Jong Lui
      For comparison of two experimental treatments with a placebo under an incomplete block crossover design, we develop the weighted-least-squares estimator (WLSE) and the conditional maximum likelihood estimator (CMLE) of the relative treatment effects in Poisson frequency data. We further develop the interval estimator based on the WLSE, the interval estimator based on the CMLE, the interval estimator based on the conditional-likelihood-ratio test and the interval estimator based on the exact conditional distribution. Using Monte Carlo simulations, we find that all interval estimators developed here can perform well in a variety of situations. The exact interval estimator derived here can be especially of use when both the number of patients and the mean number of event occurrences are small in a trial. We use the data taken as part of a double-blind randomized crossover trial comparing salbutamol and salmeterol with a placebo with respect to the number of exacerbations in asthma patients to illustrate the use of these estimators.

      PubDate: 2016-02-22T16:09:23Z
      DOI: 10.1016/j.stamet.2016.01.007
      Issue No: Vol. 32 (2016)
       
  • Testing variability orderings by using Gini’s mean differences
    • Authors: Miguel A. Sordo; Marilia C. de Souza; Alfonso Suárez-Llorens
      Pages: 63 - 76
      Abstract: Publication date: Available online 18 March 2016
      Source:Statistical Methodology
      Author(s): Miguel A. Sordo, Marilia C. de Souza, Alfonso Suárez-Llorens
      In this paper, we derive a measure of discrepancy based on the Gini’s mean difference to test the null hypothesis that two random variables, which are ordered in a variability-type stochastic order, are equally dispersive versus the alternative that one strictly dominates the other. We describe the test, evaluate its performance under a variety of situations and illustrate the procedure with an example using log returns of real data.

      PubDate: 2016-03-20T18:56:54Z
      DOI: 10.1016/j.stamet.2016.03.001
      Issue No: Vol. 32 (2016)
       
  • Homogeneity testing via weighted affinity in multiparameter exponential
           families
    • Authors: Alexander Katzur; Udo Kamps
      Pages: 77 - 90
      Abstract: Publication date: Available online 16 April 2016
      Source:Statistical Methodology
      Author(s): Alexander Katzur, Udo Kamps
      Based on stochastically independent samples with underlying density functions from the same multiparameter exponential family, a weighted version of Matusita’s affinity is applied as test statistic in a homogeneity test of identical densities as well as in a discrimination problem. Asymptotic distributions of the test statistics are stated, and the impact of weights on the deviation of actual and required type I error for finite sample sizes is examined in a simulation study.

      PubDate: 2016-04-18T13:14:17Z
      DOI: 10.1016/j.stamet.2016.04.002
      Issue No: Vol. 32 (2016)
       
  • Rank-based kernel estimation of the area under the ROC curve
    • Authors: Jingjing Yin; Yi Hao; Hani Samawi; Haresh Rochani
      Pages: 91 - 106
      Abstract: Publication date: Available online 27 April 2016
      Source:Statistical Methodology
      Author(s): Jingjing Yin, Yi Hao, Hani Samawi, Haresh Rochani
      In medical diagnostics, the ROC curve is the graph of sensitivity against 1-specificity as the diagnostic threshold runs through all possible values. The ROC curve and its associated summary indices are very useful for the evaluation of the discriminatory ability of biomarkers/diagnostic tests with continuous measurements. Among all summary indices, the area under the ROC curve (AUC) is the most popular diagnostic accuracy index, which has been extensively used by researchers for biomarker evaluation and selection. Sometimes, taking the actual measurements of a biomarker is difficult and expensive, whereas ranking them without actual measurements can be easy. In such cases, ranked set sampling based on judgment order statistics would provide more representative samples yielding more accurate estimation. In this study, Gaussian kernel is utilized to obtain a nonparametric estimate of the AUC. Asymptotic properties of the AUC estimates are derived based on the theory of U-statistics. Intensive simulation is conducted to compare the estimates using ranked set samples versus simple random samples. The simulation and theoretical derivation indicate that ranked set sampling is generally preferred with smaller variances and mean squared errors (MSE). The proposed method is illustrated via a real data analysis.

      PubDate: 2016-05-01T14:04:49Z
      DOI: 10.1016/j.stamet.2016.04.001
      Issue No: Vol. 32 (2016)
       
  • Latent class analysis of incomplete data via an entropy-based criterion
    • Authors: Chantal Larose; Ofer Harel; Katarzyna Kordas; Dipak K. Dey
      Pages: 107 - 121
      Abstract: Publication date: Available online 10 May 2016
      Source:Statistical Methodology
      Author(s): Chantal Larose, Ofer Harel, Katarzyna Kordas, Dipak K. Dey
      Latent class analysis is used to group categorical data into classes via a probability model. Model selection criteria then judge how well the model fits the data. When addressing incomplete data, the current methodology restricts the imputation to a single, pre-specified number of classes. We seek to develop an entropy-based model selection criterion that does not restrict the imputation to one number of clusters. Simulations show the new criterion performing well against the current standards of AIC and BIC, while a family studies application demonstrates how the criterion provides more detailed and useful results than AIC and BIC.

      PubDate: 2016-05-15T03:10:59Z
      DOI: 10.1016/j.stamet.2016.04.004
      Issue No: Vol. 32 (2016)
       
  • Statistical inference for a varying-coefficient partially nonlinear model
           with measurement errors
    • Authors: Yunyun Qian; Zhensheng Huang
      Pages: 122 - 130
      Abstract: Publication date: Available online 24 May 2016
      Source:Statistical Methodology
      Author(s): Yunyun Qian, Zhensheng Huang
      In this study a varying-coefficient partially nonlinear model with measurement errors in the nonparametric part is proposed. Based on the corrected profile least-squared estimation methodology, we define the estimates of the unknowns of the current models, and check whether the coefficient functions are a constant or not by using the popular generalized likelihood ratio (GLR) test method. Further, the corresponding asymptotic distribution is established and a bootstrap procedure is also employed to implement the proposed methodology. Simulated and real examples are given to illustrate our proposed methodology.

      PubDate: 2016-05-24T14:26:03Z
      DOI: 10.1016/j.stamet.2016.05.004
      Issue No: Vol. 32 (2016)
       
  • Location-scale mixture of skew-elliptical distributions: Looking at the
           robust modeling
    • Authors: N. Nematollahi; R. Farnoosh; Z. Rahnamaei
      Pages: 131 - 146
      Abstract: Publication date: Available online 2 June 2016
      Source:Statistical Methodology
      Author(s): N. Nematollahi, R. Farnoosh, Z. Rahnamaei
      A flexible class of skew-slash distributions which is a location-scale mixture of skew-elliptically distributed random variable with power of a beta random variable is presented. This family of distributions, which is a generalization of location-scale mixture of normal and beta distributions, contain some existing and important distributions and is appropriate for modeling data with skewness and heavy tail structure. Some distributional properties and the moments of this new family of distributions are obtained. In the special case of location-scale mixture of skew-normal distribution, we estimate the parameters via an EM-type algorithm and a simulation study and an application to real data are provided for illustration. Finally we extend some results to multivariate case.

      PubDate: 2016-06-04T14:59:10Z
      DOI: 10.1016/j.stamet.2016.05.001
      Issue No: Vol. 32 (2016)
       
  • Going beyond oracle property: Selection consistency and uniqueness of
           local solution of the generalized linear model
    • Authors: Chi Tim Ng; Seungyoung Oh; Youngjo Lee
      Pages: 147 - 160
      Abstract: Publication date: Available online 8 June 2016
      Source:Statistical Methodology
      Author(s): Chi Tim Ng, Seungyoung Oh, Youngjo Lee
      Recently, the selection consistency of penalized least square estimators has received a great deal of attention. For the penalized likelihood estimation with certain non-convex penalties, search space can be constructed within which there exists a unique local minimizer that exhibits selection consistency in high-dimensional generalized linear models under certain conditions. In particular, we prove that the SCAD penalty of Fan and Li (2001) and a new modified version of the unbounded penalty of Lee and Oh (2014) can be employed to achieve such a property. These results hold even for the non-sparse cases where the number of relevant covariates increases with the sample size. Simulation studies are provided to compare the performance of SCAD penalty and the newly proposed penalty.

      PubDate: 2016-06-16T18:00:21Z
      DOI: 10.1016/j.stamet.2016.05.006
      Issue No: Vol. 32 (2016)
       
  • Efficient estimation of varying coefficient models with serially
           correlated errors
    • Authors: Xiaojuan Kang; Tizheng Li
      Pages: 161 - 184
      Abstract: Publication date: Available online 14 June 2016
      Source:Statistical Methodology
      Author(s): Xiaojuan Kang, Tizheng Li
      The varying coefficient model provides a useful tool for statistical modeling. In this paper, we propose a new procedure for more efficient estimation of its coefficient functions when its errors are serially correlated and modeled as an autoregressive (AR) process. We establish the asymptotic distribution of the proposed estimator and show that it is more efficient than the conventional local linear estimator. Furthermore, we suggest a penalized profile least squares method with the smoothly clipped absolute deviation (SCAD) penalty function to select the order of the AR error process. Simulation evidence shows that significant gains can be achieved in finite samples with the proposed estimation procedure. Moreover, a real data example is given to illustrate the usefulness of the proposed estimation procedure.

      PubDate: 2016-06-16T18:00:21Z
      DOI: 10.1016/j.stamet.2016.05.005
      Issue No: Vol. 32 (2016)
       
  • Estimation and prediction for a progressively censored generalized
           inverted exponential distribution
    • Authors: Sanku Dey; Sukhdev Singh; Yogesh Mani Tripathi; A. Asgharzadeh
      Pages: 185 - 202
      Abstract: Publication date: Available online 21 June 2016
      Source:Statistical Methodology
      Author(s): Sanku Dey, Sukhdev Singh, Yogesh Mani Tripathi, A. Asgharzadeh
      In this paper, we consider generalized inverted exponential distribution which is capable of modelling various shapes of failure rates and ageing criteria. The purpose of this paper is two fold. Based on progressive type-II censored data, first we consider the problem of estimation of parameters under classical and Bayesian approaches. In this regard, we obtain maximum likelihood estimates, and Bayes estimates under squared error loss function. We also compute 95% asymptotic confidence interval and highest posterior density interval estimates under the respective approaches. Second, we consider the problem of prediction of future observations using maximum likelihood predictor, best unbiased predictor, conditional median predictor and Bayes predictor. The associated predictive interval estimates for the censored observations are computed as well. Finally, we analyze two real data sets and conduct a Monte Carlo simulation study to compare the performance of the various proposed estimators and predictors.

      PubDate: 2016-07-01T09:27:44Z
      DOI: 10.1016/j.stamet.2016.05.007
      Issue No: Vol. 32 (2016)
       
  • Systematic deviation in smooth mixed models for multi-level longitudinal
           data
    • Authors: Viani A. Biatat Djeundje
      Pages: 203 - 217
      Abstract: Publication date: Available online 27 May 2016
      Source:Statistical Methodology
      Author(s): Viani A. Biatat Djeundje
      The analysis of longitudinal data or repeated measurements is an important and growing area of Statistics. In this context, data come in different formats but typically, they have an hierarchical or multi-level structure including group and subject components, and the main purpose of the analysis is usually to estimate these components from the data. A standard way to perform this estimation is via mixed models. In this paper, we show that the estimated group effects from standard smooth mixed models can deviate systematically from the underlying group mean, leading to wrong conclusions about the data. We then present two ways to avoid such systematic deviations and misinterpretations when fitting flexible mixed models to multi-level data. The first method is a marginal procedure, and the second method is based on the conditional distribution of the subject effects derived from appropriate constraints. Both methods are robust against mis-specification of the covariance structure in the sense that they allow to resolve the lack of centering found in standard smooth mixed models.

      PubDate: 2016-05-30T14:46:14Z
      DOI: 10.1016/j.stamet.2016.05.003
      Issue No: Vol. 32 (2016)
       
  • Some new results on the LQE ordering
    • Authors: Dian-tong Kang
      Pages: 218 - 235
      Abstract: Publication date: Available online 30 June 2016
      Source:Statistical Methodology
      Author(s): Dian-tong Kang
      Ebrahimi and Pellerey (1995) and Ebrahimi (1996) proposed the residual entropy. Recently, Sunoj and Sankaran (2012) obtained a quantile version of the residual entropy, the residual quantile entropy (RQE). Base on the RQE function, they defined a new stochastic order, the less quantile entropy (LQE) order, and studied some properties of this order. In this paper, we focus on further properties of this new order. Some characterizations of the LQE order are investigated, closure and reversed closure properties are obtained, meanwhile, some illustrative examples are shown. As applications of a main result, the preservation of the LQE order in several stochastic models are discussed. We give the closure and reversed closure properties of the LQE order for coherent systems with dependent and identically distributed components, and also consider a potential application to insurance of this order.

      PubDate: 2016-07-01T09:27:44Z
      DOI: 10.1016/j.stamet.2016.06.001
      Issue No: Vol. 32 (2016)
       
  • A note on domains of attraction of the limit laws of intermediate order
           statistics under power normalization
    • Authors: H.M. Barakat; A.R. Omar
      Pages: 1 - 7
      Abstract: Publication date: Available online 19 January 2016
      Source:Statistical Methodology
      Author(s): H.M. Barakat, A.R. Omar
      In this paper we compare the domains of attraction of limit laws of intermediate order statistics under power normalization with those of limit laws of intermediate order statistics under linear normalization. As a result of this comparison, we obtain necessary and sufficient conditions for a univariate distribution function to belong to the domain of attraction for each of the possible limit laws of intermediate order statistics under power normalization.

      PubDate: 2016-01-20T16:28:12Z
      DOI: 10.1016/j.stamet.2016.01.001
      Issue No: Vol. 31 (2016)
       
  • A new skew integer valued time series process
    • Authors: Marcelo Bourguignon; Klaus L.P. Vasconcellos
      Pages: 8 - 19
      Abstract: Publication date: Available online 20 January 2016
      Source:Statistical Methodology
      Author(s): Marcelo Bourguignon, Klaus L.P. Vasconcellos
      In this paper, we introduce a stationary first-order integer-valued autoregressive process with geometric–Poisson marginals. The new process allows negative values for the series. Several properties of the process are established. The unknown parameters of the model are estimated using the Yule-Walker method and the asymptotic properties of the estimator are considered. Some numerical results of the estimators are presented with a brief discussion. Possible application of the process is discussed through a real data example.

      PubDate: 2016-01-20T16:28:12Z
      DOI: 10.1016/j.stamet.2016.01.002
      Issue No: Vol. 31 (2016)
       
  • On testing local hypotheses via local divergence
    • Authors: G. Avlogiaris; A. Micheas; K. Zografos
      Pages: 20 - 42
      Abstract: Publication date: Available online 29 January 2016
      Source:Statistical Methodology
      Author(s): G. Avlogiaris, A. Micheas, K. Zografos
      The aim of this paper is to propose procedures that test statistical hypotheses locally, that is, assess the validity of a model in a specific domain of the data. In this context, the one and two sample problems will be discussed. The proposed tests are based on local divergences which are defined in such a way as to quantify the divergence between probability distributions locally, in a specific area of the joint domain of the underlined models. The theoretical results are exemplified using simulations and two real datasets.

      PubDate: 2016-01-29T14:55:09Z
      DOI: 10.1016/j.stamet.2016.01.003
      Issue No: Vol. 31 (2016)
       
  • On a better lower bound for the frequentist probability of coverage of
           Bayesian credible intervals in restricted parameter spaces
    • Authors: Ehssan Ghashim; Éric Marchand; William E. Strawderman
      Pages: 43 - 57
      Abstract: Publication date: July 2016
      Source:Statistical Methodology, Volume 31
      Author(s): Ehssan Ghashim, Éric Marchand, William E. Strawderman
      For estimating a lower restricted parametric function in the framework of Marchand and Strawderman (2006), we show how ( 1 − α ) × 100 % Bayesian credible intervals can be constructed so that the frequentist probability of coverage is no less than 1 − 3 α 2 . As in Marchand and Strawderman (2013), the findings are achieved through the specification of the spending function of the Bayes credible interval and apply to an “equal-tails” modification of the HPD procedure among others. Our results require a logconcave assumption for the distribution of a pivot, and apply to estimating a lower bounded normal mean with known variance, and to further examples include lower bounded scale parameters from Gamma, Weibull, and Fisher distributions, with the latter also applicable to random effects analysis of variance.

      PubDate: 2016-02-26T16:18:21Z
      DOI: 10.1016/j.stamet.2016.01.006
      Issue No: Vol. 31 (2016)
       
  • Estimation of the stress-strength reliability for the two-parameter
           bathtub-shaped lifetime distribution based on upper record values
    • Authors: Bahman Tarvirdizade; Mohammad Ahmadpour
      Pages: 58 - 72
      Abstract: Publication date: Available online 10 February 2016
      Source:Statistical Methodology
      Author(s): Bahman Tarvirdizade, Mohammad Ahmadpour
      In this paper, the estimation of the stress-strength reliability Pr ( X > Y ) based on upper record values is considered when X and Y are independent random variables from a two-parameter bathtub-shaped lifetime distribution with the same shape but different scale parameters. The maximum likelihood estimator (MLE), the approximate Bayes estimator and the exact confidence intervals of stress-strength reliability are obtained when the shape parameter is known. When the shape parameter is unknown, we obtain the MLE, the asymptotic confidence interval and some bootstrap confidence intervals of stress-strength reliability. In this case, we also apply the Gibbs sampling technique to study the Bayesian estimation of stress-strength reliability and the corresponding credible interval. A Monte Carlo simulation study is conducted to investigate and compare the performance of the different proposed methods in this paper. Finally, analysis of a real data set is presented for illustrative purposes.

      PubDate: 2016-02-14T15:46:18Z
      DOI: 10.1016/j.stamet.2016.01.005
      Issue No: Vol. 31 (2016)
       
  • Autoregressive conditional negative binomial model applied to
           over-dispersed time series of counts
    • Authors: Cathy W.S. Chen; Mike K.P. So; Jessica C. Li; Songsak Sriboonchitta
      Pages: 73 - 90
      Abstract: Publication date: Available online 9 February 2016
      Source:Statistical Methodology
      Author(s): Cathy W.S. Chen, Mike K.P. So, Jessica C. Li, Songsak Sriboonchitta
      Integer-valued time series analysis offers various applications in biomedical, financial, and environmental research. However, existing works usually assume no or constant over-dispersion. In this paper, we propose a new model for time series of counts, the autoregressive conditional negative binomial model that has a time-varying conditional autoregressive mean function and heteroskedasticity. The location and scale parameters of the negative binomial distribution are flexible in the proposed set-up, inducing dynamic over-dispersion. We adopt Bayesian methods with a Markov chain Monte Carlo sampling scheme to estimate model parameters and utilize deviance information criterion for model comparison. We conduct simulations to investigate the estimation performance of this sampling scheme for the proposed negative binomial model. To demonstrate the proposed approach in modeling time-varying over-dispersion, we consider two criminal incidents recorded by New South Wales (NSW) Police Force in Australia. We also fit the autoregressive conditional Poisson model to these two datasets. Our results demonstrate that the proposed negative binomial model is preferable to the Poisson model.

      PubDate: 2016-02-14T15:46:18Z
      DOI: 10.1016/j.stamet.2016.02.001
      Issue No: Vol. 31 (2016)
       
  • Shrinkage-based semiparametric density estimation
    • Authors: S. Ejaz Ahmed; Mohamed Amezziane
      Abstract: Publication date: Available online 18 December 2016
      Source:Statistical Methodology
      Author(s): S. Ejaz Ahmed, Mohamed Amezziane
      Shrinkage estimation is used to develop a semiparametric density estimator as a linear combination of a fully known parametric density function and a nonparametric density estimator. We determine the asymptotic properties of the shrinkage coefficient and of the semiparametric estimator’s integrated squared error. Moreover, we show that the proposed estimation methodology delivers density estimators that are more accurate than nonparametric estimators and that do not require the use of optimal smoothing parameters.

      PubDate: 2016-12-19T12:02:50Z
      DOI: 10.1016/j.stamet.2016.10.004
       
  • Non-parametric Bayesian inference for continuous density hidden Markov
           mixture model
    • Authors: Najmeh Bathaee; Hamid Sheikhzadeh
      Abstract: Publication date: Available online 11 November 2016
      Source:Statistical Methodology
      Author(s): Najmeh Bathaee, Hamid Sheikhzadeh
      In this paper, we present a non-parametric continuous density Hidden Markov mixture model (CDHMMix model) with unknown number of mixtures for blind segmentation or clustering of sequences. In our presented model, the emission distributions of HMMs are chosen to be Gaussian with full, diagonal, or tridiagonal covariance matrices. We apply a Bayesian approach to train our presented model and drive the inference of our model using the Monte Carlo Markov Chain (MCMC) method. For the multivariate Gaussian emission a method that maintains the tridiagonal structure of the covariance is introduced. Moreover, we present a new sampling method for hidden state sequences of HMMs based on the Viterbi algorithm that increases the mixing rate.

      PubDate: 2016-11-13T12:08:57Z
      DOI: 10.1016/j.stamet.2016.10.003
       
  • Nonparametric M-estimation for right censored regression model with
           stationary ergodic data
    • Authors: Mohamed Chaouch; Elias Ould
      Abstract: Publication date: Available online 29 October 2016
      Source:Statistical Methodology
      Author(s): Mohamed Chaouch, Naâmane Laïb, Elias Ould Saïd
      The present paper deals with a nonparametric M -estimation for right censored regression model with stationary ergodic data. Defined as an implicit function, is a kernel-type estimator of a family of robust regression is considered when the covariate take its values in R d ( d ≥ 1 ) and the data are sampled from a stationary ergodic process. The strong consistency (with rate) and the asymptotic distribution of the estimator are established under mild assumptions. Moreover, a usable confidence interval is provided which does not depend on any unknown quantity. Our results hold without any mixing condition and do not require the existence of marginal densities. A comparison study based on simulated data is also provided.

      PubDate: 2016-10-30T11:21:46Z
       
  • A generalized inverse trinomial distribution with application
    • Authors: Shin Zhu Sim; Seng Huat Ong
      Abstract: Publication date: Available online 26 October 2016
      Source:Statistical Methodology
      Author(s): Shin Zhu Sim, Seng Huat Ong
      This paper considers a particular generalized inverse trinomial distribution which may be regarded as the convolution of binom‘ial and negative distributions for the statistical analysis of count data. This distribution has the flexibility to cater for under-, equi- and over- dispersion in the data. Some basic and probabilistic properties and tail approximation of the distribution have been derived. Conditions for the numerical stability of the two-term probability recurrence formula have also been examined to facilitate computation. For the purpose of statistical analysis, test of hypothesis for equi-dispersion by the score and likelihood ratio tests and simulation study of their power, parameter estimation by maximum likelihood and a probability generating function based methods have been considered. The versatility of the distribution is illustrated by its application to real biological data sets which exhibit under and over dispersion. It is shown that the distribution fits better than the well-known generalized Poisson and COM-Poisson distributions.

      PubDate: 2016-10-30T11:21:46Z
      DOI: 10.1016/j.stamet.2016.10.001
       
  • Estimating the integer mean of a normal model related to binomial
           distribution
    • Authors: Rasul A. Khan
      Abstract: Publication date: Available online 7 October 2016
      Source:Statistical Methodology
      Author(s): Rasul A. Khan
      A problem for estimating the number of trials n in the binomial distribution B ( n , p ) , is revisited by considering the large sample model N ( μ , c μ ) and the associated maximum likelihood estimator (MLE) and some sequential procedures. Asymptotic properties of the MLE of n via the normal model N ( μ , c μ ) are briefly described. Beyond the asymptotic properties, our main focus is on the sequential estimation of n . Let X 1 , X 2 , . . . , X m , . . . be iid N ( μ , c μ ) ( c > 0 ) random variables with an unknown mean μ = 1 , 2 , . . . and variance c μ , where c is known. The sequential estimation of μ is explored by a method initiated by Robbins (1970) and further pursued by Khan (1973). Various properties of the procedure including the error probability and the expected sample size are determined. An asymptotic optimality of the procedure is given. Sequential interval estimation and point estimation are also briefly discussed.

      PubDate: 2016-10-10T10:03:38Z
      DOI: 10.1016/j.stamet.2016.09.004
       
  • A novel power-based approach to Gaussian kernel selection in the
           kernel-based association test
    • Authors: Xiang Zhan; Debashis Ghosh
      Abstract: Publication date: Available online 6 October 2016
      Source:Statistical Methodology
      Author(s): Xiang Zhan, Debashis Ghosh
      Kernel-based association test (KAT) is a widely used tool in genetics association analysis. The performance of such a test depends on the choice of kernel. In this paper, we study the statistical power of a KAT using a Gaussian kernel. We explicitly develop a notion of analytical power function in this family of tests. We propose a novel approach to select the kernel so as to maximize the analytical power function of the test at a given test level (an upper bound on the probability of making a type I error). We assess some theoretical properties of our optimal estimator, and compare its performance with some similar existing alternatives using simulation studies. Neuroimaging data from an Alzheimer’s disease study is also used to illustrate the proposed kernel selection methodology.

      PubDate: 2016-10-10T10:03:38Z
      DOI: 10.1016/j.stamet.2016.09.003
       
  • Inference procedures about population correlations under order
           restrictions
    • Authors: Gregory E. Wilding; Mark C. Baker
      Abstract: Publication date: Available online 6 October 2016
      Source:Statistical Methodology
      Author(s): Gregory E. Wilding, Mark C. Baker
      The testing of equality of several Pearson correlations can be found in a number of scientific fields. We surmise in many such cases that the alternatives of interest in practice are, in deed, order restricted, and therefore the researcher is best served by use of testing procedures developed for those specific alternatives. In this note we introduce a collection of tests for use in testing equality of k correlation coefficients against order alternatives, with an emphasis on simple order. Specifically, we propose likelihood ratio tests and contrast tests based on the well known Fisher Z transformation as well as tests which make use of generalized variable methodologies. The proposed procedures are empirically compared with regards to type I and II error rates via Monte Carlo simulations studies, and the use of the approaches are illustrated using an example. These tests are found to be vastly superior to tests for the general alternative, and the contrasts tests based on the Fisher Z transformation are recommended for practice based on the observed test properties and simplicity.

      PubDate: 2016-10-10T10:03:38Z
      DOI: 10.1016/j.stamet.2016.09.001
       
  • Forward selection and estimation in high dimensional single index models
    • Authors: Shikai Luo; Subhashis Ghosal
      Abstract: Publication date: Available online 27 September 2016
      Source:Statistical Methodology
      Author(s): Shikai Luo, Subhashis Ghosal
      We propose a new variable selection and estimation technique for high dimensional single index models with unknown monotone smooth link function. Among many predictors, typically, only a small fraction of them have significant impact on prediction. In such a situation, more interpretable models with better prediction accuracy can be obtained by variable selection. In this article, we propose a new penalized forward selection technique which can reduce high dimensional optimization problems to several one dimensional optimization problems by choosing the best predictor and then iterating the selection steps until convergence. The advantage of optimizing in one dimension is that the location of optimum solution can be obtained with an intelligent search by exploiting smoothness of the criterion function. Moreover, these one dimensional optimization problems can be solved in parallel to reduce computing time nearly to the level of the one-predictor problem. Numerical comparison with the LASSO and the shrinkage sliced inverse regression shows very promising performance of our proposed method.

      PubDate: 2016-10-03T09:36:27Z
      DOI: 10.1016/j.stamet.2016.09.002
       
  • Sequential testing of hypotheses about drift for Gaussian diffusions
    • Authors: David
      Abstract: Publication date: Available online 22 July 2016
      Source:Statistical Methodology
      Author(s): David Stibůrek
      In statistical inference on the drift parameter θ in the process X t = θ a ( t ) + ∫ 0 t b ( s ) d W s , where a ( t ) and b ( t ) are known, deterministic functions, there is known a large number of options how to do it. We may, for example, base this inference on the differences between the observed values of the process at discrete times and their normality. Although such methods are very simple, it turns out that it is more appropriate to use sequential methods. For the hypotheses testing about the drift parameter θ , it is more proper to standardize the observed process and to use sequential methods based on the first exit time of the observed process of a pre-specified interval until some given time. These methods can be generalized to the case of random part being a symmetric Itô integral or continuous symmetric martingale.

      PubDate: 2016-07-28T08:58:50Z
       
  • A family of association measures for 2×2 contingency tables based on
           the ϕ-divergence
    • Authors: Michael Espendiller; Maria Kateri
      Abstract: Publication date: Available online 11 January 2016
      Source:Statistical Methodology
      Author(s): Michael Espendiller, Maria Kateri
      The odds ratio is the predominant measure of association in 2 × 2 contingency tables, which, for inferential purposes, is usually considered on the log-scale. Under an information theoretic set-up, it is connected to the Kullback-Leibler divergence. Considering a generalized family of divergences, the ϕ divergence, alternative association measures are derived for 2 × 2 contingency tables. Their properties are studied and asymptotic inference is developed. For some members of this family, the estimated association measures remain finite in the presence of a sampling zero while for a subset of these members the estimators of these measures have finite variance as well. Special attention is given to the power divergence, which is a parametric family. The role of its parameter λ , in terms of the asymptotic confidence intervals’ coverage probability and average relative length, is further discussed. In special probability table structures, for which the performance of the asymptotic confidence intervals for the classical log odds ratio is poor, the measure corresponding to λ = 1 / 3 is suggested as an alternative.

      PubDate: 2016-01-12T16:07:16Z
       
  • Multilevel zero-inflated Generalized Poisson regression modeling for
           dispersed correlated count data
    • Authors: Afshin Almasi; Mohammad Reza Eshraghian; Abbas Moghimbeigi; Abbas Rahimi; Kazem Mohammad; Sadegh Fallahigilan
      Pages: 1 - 14
      Abstract: Publication date: May 2016
      Source:Statistical Methodology, Volume 30
      Author(s): Afshin Almasi, Mohammad Reza Eshraghian, Abbas Moghimbeigi, Abbas Rahimi, Kazem Mohammad, Sadegh Fallahigilan
      Poisson or zero-inflated Poisson models often fail to fit count data either because of over- or underdispersion relative to the Poisson distribution. Moreover, data may be correlated due to the hierarchical study design or the data collection methods. In this study, we propose a multilevel zero-inflated generalized Poisson regression model that can address both over- and underdispersed count data. Random effects are assumed to be independent and normally distributed. The method of parameter estimation is EM algorithm base on expectation and maximization which falls into the general framework of maximum-likelihood estimations. The performance of the approach was illustrated by data regarding an index of tooth caries on 9-year-old children. Using various dispersion parameters, through Monte Carlo simulations, the multilevel ZIGP yielded more accurate parameter estimates, especially for underdispersed data.

      PubDate: 2015-12-11T14:53:16Z
      DOI: 10.1016/j.stamet.2015.11.001
      Issue No: Vol. 30 (2015)
       
  • A distribution-free test of parallelism for two-sample repeated
           measurements
    • Authors: Mehrdad Vossoughi; S.M.T. Ayatollahi Mina Towhidi Seyyed Taghi Heydari
      Abstract: Publication date: Available online 29 December 2015
      Source:Statistical Methodology
      Author(s): Mehrdad Vossoughi, S.M.T. Ayatollahi, Mina Towhidi, Seyyed Taghi Heydari
      In this paper, we propose a new two-sample distribution-free procedure for testing group-by-time interaction effect in repeated measurements from a linear mixed model setting. The test statistic is based on the maximum difference of partial sums (MDPS) over time points between the two groups. Although the test has a biomedical focus, it can be applied in fields that the study is designed and monitored to be balanced and complete with equal sample sizes as would be generally done in a controlled experiment. The asymptotic null distribution of the test statistic was also derived based on the maxima of Brownian bridge under two different conditions. The simulations revealed that MDPS performed markedly better than the commonly used unstructured multivariate approach (UMA) to profile analysis. However, the empirical powers of MDPS test were convincingly close to those of the best-fitting linear mixed model (LMM).

      PubDate: 2015-12-30T14:19:32Z
       
  • Analysis of transformation models with doubly truncated data
    • Authors: Pao-sheng Shen
      Abstract: Publication date: Available online 17 December 2015
      Source:Statistical Methodology
      Author(s): Pao-sheng Shen
      We analyze doubly truncated data using semiparametric transformation models. It is demonstrated that the extended estimating equations of Cheng et al. (1995) can be used to analyze doubly truncated data. The asymptotic properties of the proposed estimators are derived. A simulation study is conducted to investigate the performance of the proposed estimators.

      PubDate: 2015-12-18T16:34:42Z
       
  • Correlation structure of the Marshall–Olkin bivariate exponential
           distribution
    • Authors: Gwo Dong; Lin Chin-Diew Lai Govindaraju
      Abstract: Publication date: Available online 11 September 2015
      Source:Statistical Methodology
      Author(s): Gwo Dong Lin, Chin-Diew Lai, K. Govindaraju
      We first review the basic properties of Marshall–Olkin bivariate exponential distribution (BVE) and then investigate its correlation structure. We provide the correct reasonings for deriving some properties of the Marshall–Olkin BVE and show that the correlation of the BVE is always smaller than that of its copula regardless of the parameters. The latter implies that the BVE does not have Lancaster’s phenomenon (any nonlinear transformation of variables decreases the correlation in absolute value). The dependence structure of the BVE is also investigated.

      PubDate: 2015-09-12T21:54:47Z
       
 
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