A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

              [Sort alphabetically]   [Restore default list]

  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted by number of followers
Review of Economics and Statistics     Hybrid Journal   (Followers: 148)
Statistics in Medicine     Hybrid Journal   (Followers: 134)
Journal of Econometrics     Hybrid Journal   (Followers: 83)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 72, SJR: 3.746, CiteScore: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52)
Biometrics     Hybrid Journal   (Followers: 50)
Sociological Methods & Research     Hybrid Journal   (Followers: 43)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 41)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 39, SJR: 3.664, CiteScore: 2)
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 37)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 35)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 33)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 33)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 28)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 28)
The American Statistician     Full-text available via subscription   (Followers: 26)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 24)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 23)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 21)
Journal of Applied Statistics     Hybrid Journal   (Followers: 20)
Journal of Forecasting     Hybrid Journal   (Followers: 19)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 18)
Statistical Modelling     Hybrid Journal   (Followers: 18)
International Journal of Quality, Statistics, and Reliability     Open Access   (Followers: 17)
Journal of Statistical Software     Open Access   (Followers: 16, SJR: 13.802, CiteScore: 16)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 16)
Risk Management     Hybrid Journal   (Followers: 16)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 15)
Computational Statistics     Hybrid Journal   (Followers: 15)
Statistics and Computing     Hybrid Journal   (Followers: 14)
Demographic Research     Open Access   (Followers: 14)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Journal of Statistical Physics     Hybrid Journal   (Followers: 13)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 12)
International Statistical Review     Hybrid Journal   (Followers: 12)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 12)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 12)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 11)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 11)
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Journal of Probability and Statistics     Open Access   (Followers: 10)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 10)
Biometrical Journal     Hybrid Journal   (Followers: 9)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 8)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 8)
Current Research in Biostatistics     Open Access   (Followers: 8)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 8)
Stata Journal     Full-text available via subscription   (Followers: 8)
Argumentation et analyse du discours     Open Access   (Followers: 7)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 7)
Handbook of Statistics     Full-text available via subscription   (Followers: 7)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 7)
Lifetime Data Analysis     Hybrid Journal   (Followers: 7)
Queueing Systems     Hybrid Journal   (Followers: 7)
Research Synthesis Methods     Hybrid Journal   (Followers: 7)
Significance     Hybrid Journal   (Followers: 7)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
Optimization Methods and Software     Hybrid Journal   (Followers: 6)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
Journal of Global Optimization     Hybrid Journal   (Followers: 6)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 6)
Statistical Methods and Applications     Hybrid Journal   (Followers: 6)
Law, Probability and Risk     Hybrid Journal   (Followers: 6)
Engineering With Computers     Hybrid Journal   (Followers: 5)
CHANCE     Hybrid Journal   (Followers: 5)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 5)
Applied Categorical Structures     Hybrid Journal   (Followers: 4)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
ESAIM: Probability and Statistics     Open Access   (Followers: 4)
Metrika     Hybrid Journal   (Followers: 4)
Statistical Papers     Hybrid Journal   (Followers: 4)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 3)
Sankhya A     Hybrid Journal   (Followers: 3)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 3)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Stochastic Models     Hybrid Journal   (Followers: 2)
Building Simulation     Hybrid Journal   (Followers: 2)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
Optimization Letters     Hybrid Journal   (Followers: 2)
TEST     Hybrid Journal   (Followers: 2)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
Extremes     Hybrid Journal   (Followers: 2)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
International Journal of Stochastic Analysis     Open Access   (Followers: 2)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Statistics and Economics     Open Access  
Review of Socionetwork Strategies     Hybrid Journal  
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
Journal of the Korean Statistical Society     Hybrid Journal  
Sequential Analysis: Design Methods and Applications     Hybrid Journal  

              [Sort alphabetically]   [Restore default list]

Similar Journals
Journal Cover
Journal of the Korean Statistical Society
Journal Prestige (SJR): 0.545
Citation Impact (citeScore): 1
Number of Followers: 0  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1226-3192 - ISSN (Online) 2005-2863
Published by Elsevier Homepage  [2906 journals]
  • A note on maximum likelihood estimation for mixture models

    • Free pre-print version: Loading...

      Abstract: Abstract Practitioners as well as some statistics students often blindly use standard software or algorithms to get maximum likelihood estimator (MLE) without checking the validity of existence of such an estimator. Even in simple situations where data comes from mixtures of Gaussians, global MLE does not exist. This note is intended as a teachers corner, highlighting existential issues related to MLE for mixture models, even when the components are not necessarily Gaussian.
      PubDate: 2022-07-23
       
  • Revisiting feature selection for linear models with FDR and power
           guarantees

    • Free pre-print version: Loading...

      Abstract: Abstract The problem of feature selection for linear models is re-examined by using the fixed-X knockoff procedure and incorporating the selection probability as variable importance scores. Unlike previous work that predominantly focused on false discovery rate (FDR) control, this paper aims to establish theoretical power guarantees for the fixed-X knockoff procedure in linear models. An intersection selection procedure is proposed to make better use of sample data for estimating the selection probability, which in practice results in increasing the selection power. In addition, a two-stage procedure by using the data splitting technique is further developed to explore related theoretical results under high dimensionality. The performance of the proposal over its main competitors is demonstrated through comprehensive simulation studies and real data analysis.
      PubDate: 2022-07-23
       
  • Projective resampling estimation of informative predictor subspace for
           multivariate regression

    • Free pre-print version: Loading...

      Abstract: Abstract In this paper, a paradigm to estimate the so-called informative predictor subspace (Yoo in Statistics 50:1086–1099, 2016) for multivariate regression is proposed. For this, as a primary target subspace, a projective resampling informative predictor subspace is newly developed. The projective resampling informative predictor subspace is constructed based on a projection resampling method by Li et al. (2008), and it has advantage that it is smaller than the original informative predictor subspace but contains the central subspace. To estimate the new target subspace, the three approaches of projective resampling, coordinate, and coordinate-projective resampling mean methods are proposed. The three methods are investigated via various numerical studies, which confirm their potential usefulness in practice.
      PubDate: 2022-07-18
       
  • Partial linear regression of compositional data

    • Free pre-print version: Loading...

      Abstract: Abstract We study a partial linear model in which the response is compositional and the predictors include both compositional and Euclidean variables. We define a partial linear regression model under Aitchison geometry based on isometric log-ratio (ilr) transformation. An identification condition of linear parameters is provided in terms of expectations and conditional expectations of the response and covariates. An estimator based on the identification is developed and asymptotic properties of proposed estimators are derived. The proposed method can be implemented easily by using existing R packages such as np and compositions. The limiting distribution of the proposed estimator is provided with normal distribution in Euclidean space so that it is easy to use for inference. Also, some finite sample properties are presented via simulation studies. We also present election data as an illustrative example.
      PubDate: 2022-07-15
       
  • Estimation of the parameters of a Wishart extension on symmetric matrices

    • Free pre-print version: Loading...

      Abstract: Abstract This paper deals with the parameters of a natural extension of the Wishart distribution, that is the Riesz distribution on the space of symmetric matrices. We estimate the shape parameter using two different approaches. The first one is based on the method of moments, we give its expression and investigate some of its properties. The second represents the maximum likelihood estimator. Unfortunately, in this case we do not have an explicit formula for this estimator. This latter is expressed in terms of the digamma function and sample mean of log-gamma variables. However, we derive the strong consistency and asymptotic normality properties of this estimator. A numerical comparative study between the two estimators is carried out in order to test the performance of the proposed approaches. For the second parameter, that is the scale parameter, we prove that the distribution of the maximum likelihood estimator given by Kammoun et al. (J Statist Prob Lett 126:127–131, 2017) is related to the Riesz distribution. We examine some properties concerning this estimator and we assess its performance by a numerical study.
      PubDate: 2022-07-06
       
  • Robust estimation for a general functional single index model via quantile
           regression

    • Free pre-print version: Loading...

      Abstract: Abstract This paper studies the estimation of a general functional single index model, in which the conditional distribution of the response depends on the functional predictor via a functional single index structure. We find that the slope function can be estimated consistently by the estimation obtained by fitting a misspecified functional linear quantile regression model under some mild conditions. We first obtain a consistent estimator of the slope function using functional linear quantile regression based on functional principal component analysis, and then employ a local linear regression technique to estimate the conditional quantile function and establish the asymptotic normality of the resulting estimator for it. The finite sample performance of the proposed estimation method is studied in Monte Carlo simulations, and is illustrated by an application to a real dataset.
      PubDate: 2022-06-24
       
  • Autocovariance estimation in the presence of changepoints

    • Free pre-print version: Loading...

      Abstract: Abstract This article studies estimation of a stationary autocovariance structure in the presence of an unknown number of mean shifts. Here, a Yule–Walker moment estimator for the autoregressive parameters in a dependent time series contaminated by mean shift changepoints is proposed and studied. The estimator is based on first order differences of the series and is proven consistent and asymptotically normal when the number of changepoints m and the series length N satisfy \(m/N \rightarrow 0\) as \(N \rightarrow \infty\) .
      PubDate: 2022-06-06
       
  • Fast quantile regression in reproducing kernel Hilbert space

    • Free pre-print version: Loading...

      Abstract: Abstract In literature, the idea of kernel machine was introduced to quantile regression, resulting kernel quantile regression (KQR) model, which is capable to fit nonlinear models with flexibility. However, the formulation of KQR leads to a quadratic programming which is computationally expensive to solve. This paper proposes a fast training algorithm for KQR based on majorization-minimization approach, in which an upper bound for the objective function is derived in each iteration which is easier to be minimized. The proposed approach is easy to implement, without requiring any special computing package other than basic linear algebra operations. Numerical studies on simulated and real-world datasets show that, compared to the original quadratic programming based KQR, the proposed approach can achieve essentially the same prediction accuracy with substantially higher time efficiency in training.
      PubDate: 2022-06-01
       
  • Bayesian inference of clustering and multiple Gaussian graphical models
           selection

    • Free pre-print version: Loading...

      Abstract: Abstract We consider a Bayesian framework for clustering the high-dimensional data and learning sparse multiple graphical models simultaneously. Different from most previous multiple graphs learning methods which assume that the cluster information is known in advance, we impose a multi-distribution prior for the cluster labels. Then a joint spike-and-slab graphical lasso prior is imposed for the precision matrices, which can induce a sparsity and homogeneity of the heterogeneous graphical models across all clusters adaptively. Additionally, by imposing a structural Markov random field (MRF) prior, the proposed method can also cluster the network-linked data without the independence assumption of the samples. Then a fast Expectation Maximization (EM) algorithm is utilized for the posterior inference. The proposed model can get a significant improvement both in clustering error and graphical selection precision. The simulations and real data analysis are shown to demonstrate the performance of our method.
      PubDate: 2022-06-01
       
  • Quantiles naïve, ratio and difference estimators for efficient
           stratified sampling designs

    • Free pre-print version: Loading...

      Abstract: Abstract This paper proposes and investigates the bivariate, the marginal distribution functions and quantiles estimators and their asymptotic properties for naïve, ratio, and difference estimators based on the bivariate stratified simple random sampling (BVSSRS) and bivariate stratified ranked set sampling designs (BVSRSS). We demonstrate that the proposed estimators using BVSRSS and BVSSRS are consistent and asymptotically normally distributed. Improved performance of the proposed estimators using BVSRSS compared to BVSSRS supported through an intensive simulation study. The derivation of the optimal allocation based on BVSSRS and BVSRSS is provided. The National Health and Nutrition Examination Survey (NHANES) data is used to illustrate the methods.
      PubDate: 2022-06-01
       
  • A generalized Agresti–Coull type confidence interval for a binomial
           proportion

    • Free pre-print version: Loading...

      Abstract: Abstract One of the fundamental topics in statistical inference is constructing a confidence interval for a binomial proportion p. It is well known that commonly used asymptotic confidence intervals, such as the Wilson and Agresti–Coull confidence intervals, suffer from systematic bias and oscillations in their coverage probabilities. We generalize asymptotic confidence intervals, including the Wald, Wilson and Agresti–Coull intervals, and propose a generalized Agresti–Coull type confidence interval by adjusting the bias with the saddlepoint approximation. We compare the coverage probabilities and lengths of the proposed confidence interval with those of other popular asymptotic confidence intervals. We show that the proposed confidence interval is more stable than the Wilson interval at the boundaries of p and has a shorter length than the Agresti–Coull interval.
      PubDate: 2022-06-01
       
  • Smoothed partially linear quantile regression with nonignorable missing
           response

    • Free pre-print version: Loading...

      Abstract: Abstract In this paper, we propose a smoothed estimator and variable selection method for partially linear quantile regression models with nonignorable missing responses. To address the identifiability problem, a parametric propensity model and an instrumental variable are used to construct sufficient instrumental estimating equations. Subsequently, the nonparametric function is approximated by B-spline basis functions and the kernel smoothing idea is used to make estimation statistically and computationally efficient. To accommodate the missing response and apply the popular empirical likelihood (EL) to obtain an unbiased estimator, we construct bias-corrected and smoothed estimating equations based on the inverse probability weighting approach. The asymptotic properties of the maximum EL estimator for the parametric component and the convergence rate of the estimator for the nonparametric function are derived. In addition, the variable selection in the linear component based on the penalized EL is also proposed. The finite-sample performance of the proposed estimators is studied through simulations, and an application to HIV-CD4 data set is also presented.
      PubDate: 2022-06-01
       
  • Empirical likelihood for spatial dynamic panel data models

    • Free pre-print version: Loading...

      Abstract: Abstract Spatial dynamic panel data (SDPD) models have received great attention in economics in recent 10 years. Existing approaches for the estimation and test of SDPD models are quasi-maximum likelihood (QML) approach and generalized method of moments (GMM). In this article, we introduce the empirical likelihood (EL) method to the statistical inference for SDPD models. The EL ratio statistics are constructed for the parameters of spatial dynamic panel data models. It is shown that the limiting distributions of the empirical likelihood ratio statistics are chi-squared distributions, which are used to construct confidence regions for the parameters of the models. Simulation results show that the EL based confidence regions outperform the normal approximation based confidence regions.
      PubDate: 2022-06-01
       
  • Regularized linear censored quantile regression

    • Free pre-print version: Loading...

      Abstract: Abstract For right-censored survival data, censored quantile regression is emerging as an attractive alternative to the Cox’s proportional hazards and the accelerated failure time models. Censored quantile regression has been considered as a robust and flexible alternative in the sense that it can capture a variety of treatment effects at different quantile levels of survival function. In this paper, we present a novel regularized estimation and variable selection procedure for censored quantile regression model. Statistical inference on censored quantile regression is often based on a martingale-based estimating function that may require a strict linearity assumption and a grid-search procedure. Instead, we employ a local kernel-based Kaplan–Meier estimator and modify the quantile loss function to facilitate censored observations. This approach allows us to assume the linearity condition only at the particular quantile level of interest. Our proposed method is then regularized by using LASSO and adaptive LASSO, along with sufficient dimension reduction, to select a subset of informative covariates in a high-dimension setting. The asymptotic properties of the proposed estimators are rigorously studied. Their finite-sample properties and practical utility are explored via simulation studies and application to PBC data.
      PubDate: 2022-06-01
       
  • A random model for the scale parameter in the Fréchet populations

    • Free pre-print version: Loading...

      Abstract: Abstract This paper deals with one-way classification analysis when the response variable follows the one-parameter Fréchet distribution and the factor effects are random. The stochastic properties of the response variable are studied in detail. Maximum likelihood estimations of the model parameters are also given in explicit expressions. Under the square error loss function, the best predictions for the random-effects are derived. Three procedures for testing the hypothesis of population homogeneity are proposed and misspecification problem is investigated in a special case. Several illustrative examples are also given to assess the performances of the proposed model. Findings of this paper may be used in engineering, survival and, longitudinal studies.
      PubDate: 2022-06-01
       
  • A generalized entropy optimization modelling in the theory of stochastic
           differential equations

    • Free pre-print version: Loading...

      Abstract: Abstract In this study, we have developed one new approximate method to obtain a probability density function of a solution of a given stochastic differential equation (SDE) at a fixed time. The mentioned method is based on the estimation SDE fitting to given statistical data and approximate methods solving SDE. For this purpose, by approximate methods solving SDE trajectories of this equation are constructed. For example, it is possible to use the Euler–Maruyama (EM) method. By using trajectories at a fixed time are obtained reasonable random variables of the solution of SDE. The probability density function of the mentioned random variables is obtained. It is possible to use different statistical methods. These results are acquired by using the theorem. In our investigation, it is used Generalized Entropy Optimization Methods (GEOM). The reason using GEOM’s is explained oneself by the fact that these methods represent distributions that are more flexible distributions. We illustrated the use of this new method to apply the SDE model fitting on S&P 500 stock data.
      PubDate: 2022-06-01
       
  • A bivariate extension of three-parameter generalized crack distribution
           for loss severity modelling

    • Free pre-print version: Loading...

      Abstract: Abstract In this paper, we introduce a bivariate extension of three-parameter generalized crack distribution for modelling loss data. Some basic properties such as the conditional distribution and the measures of association are discussed, and a method of parameter estimation is offered. A simulation-based approach to compute bivariate value-at-risk under the model is also discussed. The proposed model and estimation method are illustrated with a model fitting exercise on a real catastrophic loss data set.
      PubDate: 2022-06-01
       
  • Test for conditional quantile change in GARCH models

    • Free pre-print version: Loading...

      Abstract: Abstract In this study, we consider the problem of detecting a change point in the conditional quantile of GARCH models. The task is essential in risk management as the conditional quantile is utilized to calculate the value-at-risk (VaR) of asset prices. We propose the cumulative sum (CUSUM) tests based on the residuals and derive their limiting distributions under mild conditions. We also demonstrate the validity of the tests by conducting Monte Carlo simulations, followed by a real data analysis of the exchange rate between the US Dollar and Korean Won and the Korea composite stock price index.
      PubDate: 2022-06-01
       
  • Testing linear hypothesis of high-dimensional means with unequal
           covariance matrices

    • Free pre-print version: Loading...

      Abstract: Abstract In this paper, we propose a new scalar-transformation-invariant test for linear hypothesis on mean vectors of normal population with unequal covariance matrices in high-dimensional data. The asymptotic null and non-null distributions of our new test are obtained under some regularity conditions. The performance of the proposed test is conducted by numerical simulation and a real data example, which illustrates our new test outperforms competitors in the considered cases. Moreover, numerical studies show that our new test can also be applied to non-normal data.
      PubDate: 2022-06-01
       
  • Spatial regression with non-parametric modeling of Fourier coefficients

    • Free pre-print version: Loading...

      Abstract: Abstract We consider modeling of Fourier coefficients, known as a spectral density function to represent spatial dependence of a stationary spatial random field and use it for spatial regression under a Bayesian framework. Especially, we switch from the space domain to the frequency domain and introduce a Gaussian process prior to the log spectral density. As we do not impose any further assumption on log spectral density, resulting covariance function is not of a parametric form and/or isotropic assumption. Simulation study supports that our approach is robust over various parametric covariance models. Also, our approach gives comparable or better prediction results over conventional spatial prediction under most parametric covariance models that we considered. Even though we need to estimate spectral density at all Fourier frequencies during the Bayesian procedure, our approach does not lose much computational efficiency compared to estimating only a few parameters in the parametric covariance models. We also compare our approach with some other existing spatial prediction approaches using two datasets of Korean ozone concentration. Our approach performs reasonably good in terms of mean absolute error and root mean squared error.
      PubDate: 2022-06-01
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 44.200.175.255
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-