Subjects -> STATISTICS (Total: 130 journals)
 Showing 1 - 151 of 151 Journals sorted alphabetically Advances in Complex Systems       (Followers: 10) Advances in Data Analysis and Classification       (Followers: 52) Applied Categorical Structures       (Followers: 4) Argumentation et analyse du discours       (Followers: 7) Asian Journal of Mathematics & Statistics       (Followers: 8) AStA Advances in Statistical Analysis       (Followers: 2) Australian & New Zealand Journal of Statistics       (Followers: 12) Biometrical Journal       (Followers: 9) Biometrics       (Followers: 51) British Journal of Mathematical and Statistical Psychology       (Followers: 17) Building Simulation       (Followers: 2) CHANCE       (Followers: 5) Communications in Statistics - Simulation and Computation       (Followers: 9) Communications in Statistics - Theory and Methods       (Followers: 11) Computational Statistics       (Followers: 15) Computational Statistics & Data Analysis       (Followers: 35) Current Research in Biostatistics       (Followers: 8) Decisions in Economics and Finance       (Followers: 12) Demographic Research       (Followers: 14) Engineering With Computers       (Followers: 5) Environmental and Ecological Statistics       (Followers: 7) ESAIM: Probability and Statistics       (Followers: 4) Extremes       (Followers: 2) Fuzzy Optimization and Decision Making       (Followers: 8) Geneva Papers on Risk and Insurance - Issues and Practice       (Followers: 11) Handbook of Numerical Analysis       (Followers: 5) Handbook of Statistics       (Followers: 7) IEA World Energy Statistics and Balances -       (Followers: 2) International Journal of Computational Economics and Econometrics       (Followers: 6) International Journal of Quality, Statistics, and Reliability       (Followers: 17) International Journal of Stochastic Analysis       (Followers: 2) International Statistical Review       (Followers: 12) Journal of Algebraic Combinatorics       (Followers: 3) Journal of Applied Statistics       (Followers: 20) Journal of Biopharmaceutical Statistics       (Followers: 23) Journal of Business & Economic Statistics       (Followers: 38, SJR: 3.664, CiteScore: 2) Journal of Combinatorial Optimization       (Followers: 7) Journal of Computational & Graphical Statistics       (Followers: 21) Journal of Econometrics       (Followers: 82) Journal of Educational and Behavioral Statistics       (Followers: 7) Journal of Forecasting       (Followers: 19) Journal of Global Optimization       (Followers: 6) Journal of Mathematics and Statistics       (Followers: 6) Journal of Nonparametric Statistics       (Followers: 6) Journal of Probability and Statistics       (Followers: 10) Journal of Risk and Uncertainty       (Followers: 34) Journal of Statistical and Econometric Methods       (Followers: 3) Journal of Statistical Physics       (Followers: 13) Journal of Statistical Planning and Inference       (Followers: 7) Journal of Statistical Software       (Followers: 16, SJR: 13.802, CiteScore: 16) Journal of the American Statistical Association       (Followers: 72, SJR: 3.746, CiteScore: 2) Journal of the Korean Statistical Society Journal of the Royal Statistical Society Series C (Applied Statistics)       (Followers: 36) Journal of the Royal Statistical Society, Series A (Statistics in Society)       (Followers: 28) Journal of the Royal Statistical Society, Series B (Statistical Methodology)       (Followers: 41) Journal of Theoretical Probability       (Followers: 3) Journal of Time Series Analysis       (Followers: 16) Journal of Urbanism: International Research on Placemaking and Urban Sustainability       (Followers: 23) Law, Probability and Risk       (Followers: 6) Lifetime Data Analysis       (Followers: 7) Mathematical Methods of Statistics       (Followers: 4) Measurement Interdisciplinary Research and Perspectives       (Followers: 1) Metrika       (Followers: 4) Monthly Statistics of International Trade - Statistiques mensuelles du commerce international       (Followers: 3) Multivariate Behavioral Research       (Followers: 8) Optimization Letters       (Followers: 2) Optimization Methods and Software       (Followers: 6) Oxford Bulletin of Economics and Statistics       (Followers: 33) Pharmaceutical Statistics       (Followers: 16) Queueing Systems       (Followers: 7) Research Synthesis Methods       (Followers: 7) Review of Economics and Statistics       (Followers: 138) Review of Socionetwork Strategies Risk Management       (Followers: 17) Sankhya A       (Followers: 3) Scandinavian Journal of Statistics       (Followers: 9) Sequential Analysis: Design Methods and Applications Significance       (Followers: 7) Sociological Methods & Research       (Followers: 40) SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques Stata Journal       (Followers: 8) Statistica Neerlandica       (Followers: 1) Statistical Inference for Stochastic Processes       (Followers: 3) Statistical Methods and Applications       (Followers: 6) Statistical Methods in Medical Research       (Followers: 27) Statistical Modelling       (Followers: 18) Statistical Papers       (Followers: 4) Statistics & Probability Letters       (Followers: 13) Statistics and Computing       (Followers: 13) Statistics and Economics Statistics in Medicine       (Followers: 122) Statistics: A Journal of Theoretical and Applied Statistics       (Followers: 12) Stochastic Models       (Followers: 2) Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports       (Followers: 2) Structural and Multidisciplinary Optimization       (Followers: 10) Teaching Statistics       (Followers: 8) Technology Innovations in Statistics Education (TISE)       (Followers: 2) TEST       (Followers: 2) The American Statistician       (Followers: 25) The Canadian Journal of Statistics / La Revue Canadienne de Statistique       (Followers: 10) Wiley Interdisciplinary Reviews - Computational Statistics       (Followers: 1)
Similar Journals
 Journal of the Korean Statistical SocietyJournal Prestige (SJR): 0.545 Citation Impact (citeScore): 1Number of Followers: 0      Hybrid journal (It can contain Open Access articles) ISSN (Print) 1226-3192 - ISSN (Online) 2005-2863 Published by Elsevier  [2906 journals]
• Bayesian empirical likelihood inference for the generalized binomial AR(1)
model

Abstract: In this paper, the Bayesian empirical likelihood (BEL) inference is considered for the generalized binomial AR(1) model. We establish a nonparametric likelihood using the empirical likelihood (EL) approach and consider a specific prior based on copulas. An efficient Markov chain Monte Carlo (MCMC) procedure is described for the required computation of the posterior distribution. In the simulation study, we analyze the accuracy and sensitivity of the MCMC algorithm. We also study the robustness of the new method. The results imply that our algorithm converges quickly and not strongly influenced by the model assumptions. Furthermore, the BEL method is robust. Finally, a real data example is analyzed to illustrate of our method.
PubDate: 2022-05-09

• On robustness of the relative belief ratio and the strength of its
evidence with respect to the geometric contamination prior

Abstract: Abstract The relative belief ratio becomes a widespread tool in many hypothesis testing problems. It measures the statistical evidence that a given statement is true based on a combination of data, model and prior. Additionally, a measure of the strength is used to calibrate its value. In this paper, robustness of the relative belief ratio and its strength to the choice of the prior is studied. Specifically, the Gâteaux derivative is used to measure their sensitivity when the geometric contaminated prior is used. Examples are presented to illustrate the results.
PubDate: 2022-05-03

• Infinite diameter confidence sets in Hedges’ publication bias model

Abstract: Abstract Meta-analysis, the statistical analysis of results from separate studies, is a fundamental building block of science. But the assumptions of classical meta-analysis models are not satisfied whenever publication bias is present, which causes inconsistent parameter estimates. Hedges’ selection function model takes publication bias into account, but estimating and inferring with this model is tough for some datasets. Using a generalized Gleser–Hwang theorem, we show there is no confidence set of guaranteed finite diameter for the parameters of Hedges’ selection model. This result provides a partial explanation for why inference with Hedges’ selection model is fraught with difficulties.
PubDate: 2022-04-22

• Evaluating the adequacy of variance function using pairwise distances

Abstract: Abstract In this article, we develop a distance-based testing for assessing the adequacy of conditional variance function using pairwise distances. Under the null hypothesis, we state the limiting distribution of the proposed statistic, which is complicated. A resampling type statistic is proposed for approximating the null distribution, and we prove the validity of the resampling algorithm. The test could detect any local alternatives at a nearly optimal rate. Simulation studies are provided to examine the numerical performance of the proposed test, and a real data example is illustrated its application.
PubDate: 2022-04-19

• Statistical inference for Cox model under case-cohort design with subgroup
survival information

Abstract: Abstract With the explosive growth of data, it is a challenge to infer the quantity of interest by combining the existing different research data about the same topic. In the case-cohort setting, our aim is to improve the efficiency of parameter estimation for Cox model by using subgroup information in the aggregate data. So we put forward the generalized moment method (GMM) to use the auxiliary survival information at some critical time points. However, the auxiliary information is likely obtained from other studies or populations, two extended GMM estimators are proposed to account for multiplicative and additive inconsistencies. We establish the consistency and asymptotic normality of the proposed estimators. In addition, the uniform consistency and asymptotic normality of Breslow estimator are also presented. From the asymptotic normality, we show that the proposed approaches are more efficient than the traditional weighted estimating equation method. In particular, if the number of subgroups is equal to one, the asymptotic variance-covariances of the GMM estimators are identical with the weighted score estimate. Some simulation studies and a real data study demonstrate the proposed methods and theories. In the numerical studies, our approaches are even better than the full cohort estimator and the extended GMM methods are robust.
PubDate: 2022-03-28

• Asymptotic approximations for some distributions of ratios

Abstract: Abstract We give strong large deviation results for some ratio distributions. Then we apply these results to two statistical examples: a ratio distribution with sums of gamma-distributed random variables and another one with sums of $$\chi ^2$$ -distributed random variables. We eventually carry out numerical comparisons with a saddlepoint approximation using an indirect Edgeworth expansion and a Lugannani and Rice saddlepoint approximation.
PubDate: 2022-03-22

• Multivariate response regression with low-rank and generalized sparsity

Abstract: Abstract In this study, we propose a multivariate-response regression by imposing structural conditions on the underlying regression coefficient matrix motivated by an analysis of Cancer Cell Line Encyclopedia (CCLE) data consisting of resistance responses to multiple drugs and gene expression of cancer cell lines. It is important to estimate the drug resistance response from gene information and identify those genes responsible for the sensitivity of the resistance response to each drug. We consider a penalized multiple-response regression estimator using both generalized $$\ell _1$$ norm and nuclear norm regularizers based on the motivations that only a few genes are relevant to the effect of drug resistance responses and that some genes could have similar effects on multiple responses. For the statistical properties, we developed non-asymptotic error bounds of the proposed estimator. In our numerical analysis using simulated and CCLE data, the proposed method better predicts the drug responses than the other methods.
PubDate: 2022-03-03

• Bootstrap based goodness-of-fit tests for binary multivariate regression
models

Abstract: Abstract We consider a binary multivariate regression model where the conditional expectation of a binary variable given a higher-dimensional input variable belongs to a parametric family. Based on this, we introduce a model-based bootstrap (MBB) for higher-dimensional input variables. This test can be used to check whether a sequence of independent and identically distributed observations belongs to such a parametric family. The approach is based on the empirical residual process introduced by Stute (Ann Statist 25:613–641, 1997). In contrast to Stute and Zhu’s approach (2002) Stute & Zhu (Scandinavian J Statist 29:535–545, 2002), a transformation is not required. Thus, any problems associated with non-parametric regression estimation are avoided. As a result, the MBB method is much easier for users to implement. To illustrate the power of the MBB based tests, a small simulation study is performed. Compared to the approach of Stute & Zhu (Scandinavian J Statist 29:535–545, 2002), the simulations indicate a slightly improved power of the MBB based method. Finally, both methods are applied to a real data set.
PubDate: 2022-03-01

• Nonparametric local linear regression estimation for censored data and
functional regressors

Abstract: Abstract In this work, we introduce a local linear nonparametric estimation of the regression function of a censored scalar response random variable, given a functional random covariate. Under standard conditions, we establish the pointwise and the uniform almost-complete convergences, with rates, of the proposed estimator. Then, we carry out a simulation study and a real data analysis in order to compare the performances of our methodology with those of the kernel method.
PubDate: 2022-03-01

• Residuals in GMANOVA–MANOVA model with rank restrictions on
parameters

Abstract: Abstract Residuals in the GMANOVA–MANOVA model with rank restrictions on the mean parameters is considered. The main objective is to define residuals useful for evaluating the reduced rank restriction model. We decompose linear spaces into four subspaces as it can be done for the Extended Growth Curve model with two “profiles”. The new residuals are defined by orthogonal projections on these subspaces. It is discussed how the new residuals can be used to test model assumptions.
PubDate: 2022-03-01

• Evaluating the failure risk with and without failure data

Abstract: Abstract Traditionally, the risk priority number (RPN) is used to compute the failure risk by multiplying occurrence, detection, and severity factors. Claiming that the key feature of multiplying the three factors together to get the RPN is a limitation of this method, existing studies have developed the multiple criteria decision making (MCDM) approach. In this paper, we first show that the multiplication of the three factors is indeed useful not only for evaluating the failure risk based on the trade-off between the improvement cost and risk reduction but also for identifying an appropriate action to reduce the risk of a fixed failure only if failure data is available for evaluating each of the three factors. We then develop a modified method to use the well-established multiplication operation even when each factor is evaluated by an expert. A numerical example is presented to illustrate the advantage of the modified method over the previous MCDM approach in determining the effectiveness of action plans for system risk reduction when only qualitative data is available.
PubDate: 2022-03-01

• Perturbations of copulas and mixing properties

Abstract: Abstract This paper explores the impact of perturbations of copulas on the dependence properties of the Markov chains they generate. We consider Markov chains generated by perturbations of copulas. Results are provided for the mixing coefficients $$\beta _n$$ , $$\psi _n$$ and $$\phi _n$$ . Several results on mixing for the considered perturbations are provided. New copula functions are provided in connection with perturbations of variables that induce other types of perturbation of copulas not considered in the literature.
PubDate: 2022-03-01

• Self-weighted quantile estimation of autoregressive conditional duration
model

Abstract: Abstract An efficient market is often related to the market liquidity in a certain sense. In this paper, the autoregressive conditional duration (ACD) model is used for modeling and analyzing the market liquidity based on high-frequency financial data, which takes the volume duration as its measure index. Considering the high peak and heavy tail of high-frequency financial data, the self-weighted quantile regression (SQR) estimators for the unknown parameters in ACD model are constructed. The consistency and asymptotic properties of the estimators are proved. Furthermore, Monte Carlo simulation shows that the SQR estimators with data-driven weights are more accurate than those by traditional quantile regression (QR). Moreover, the performance of SQR estimation performs better with the increase of the proportion of outliers. The mean deviation and mean square error are reduced up to 96.24% and 91.83%, respectively. Finally, we illustrate the SQR method by an empirical analysis of the volume duration for Industrial And Commercial Bank Of China (ICBC) and PingAn Bank stocks in China. Through the Akaike Information Criterion (AIC) and other evaluation criteria, the SQR estimators at different quantiles all possess better performance.
PubDate: 2022-03-01

• Inference on a structural break in trend with mildly integrated errors

Abstract: Abstract In this paper, we study a regression model with a break in trend regressor, in which the model errors are assumed to be mildly integrated. To be precise, we suppose the model errors are generated by an AR(1) process with the autoregressive coefficient $$\rho _{T}=1+{c}/{k_{T}}$$ , where T is the sample size, c is a negative constant, and $$\{k_T, T\in {\mathbb {N}}\}$$ is a sequence of positive constants diverging to infinity such that $$k_T=o(T)$$ . We estimate the break date/break fraction and other parameters in the model using the least squares method. The asymptotic properties, including the consistency, rates of convergence as well as the limiting distributions, of the estimates are examined. The results derived in this paper bridge the findings in Perron and Zhu (Journal of Econometrics 129:65–119, 2005) who estimated the break date/break fraction in trend regressor under I(0) and I(1) model errors. We also show that the phase transition for the estimation error of the least squares estimate of the break date occurs when $$k_{T}$$ has the same order of magnitude as $$T^{1/2}$$ . Monte Carlo simulations and an empirical study are given to illustrate the finite-sample performance of estimates.
PubDate: 2022-03-01

• An empirical likelihood check with varying coefficient fixed effect model
with panel data

Abstract: Abstract Semiparametric models are often used to analyze panel data for a good trade-off between parsimony and flexibility. In this paper, we investigate a fixed effect model with a possible varying coefficient component. On the basis of empirical likelihood method, the coefficient functions are estimated as well as their confidence intervals. The estimation procedures are easily implemented. An important problem of the statistical inference with the varying coefficient model is to check the constant coefficient about the regression functions. We further develop checking procedures by constructing empirical likelihood ratio statistics and establishing the Wilks theorems. Finally, some numerical simulations and a real data analysis is presented to assess the finite sample performance.
PubDate: 2022-03-01

• Applications of competing risks analysis in public health

Abstract: Abstract In medical and public health research, survival statistics are of particular interest as they can reflect patient prognoses and improvements in health care systems. However, measures of survival differ in their use and interpretation depending on how they deal with competing causes of death. Cause-specific survival estimates survival function based on the event of interest while treating other events as censored, thereby representing the “net” impact of cancer on survival. On the other hand, cumulative incidence function and subdistribution hazard consider the number of subjects experiencing competing risks in their formulations, thereby providing measures for investigating the patients’ actual prognoses. In this paper, we review competing risks survival models used in public health study. We introduce the concept of competing risks methods and compare these with traditional net approaches (e.g. relative and cause-specific). We demonstrate how competing risks analysis can be used in population-based cancer survival analysis utilizing the Surveillance, Epidemiology, and End Result (SEER) cancer registry data. As the scope of public health study has extended beyond prognosis and risk prediction, competing risks analysis has been applied in such studies as well. We further discuss the uptake of the competing risks approach in personalized and precision medicine. Various methods and applications used in risk predicting prognostic models with competing risks are reviewed, aiming to provide effective analytical tools for researchers who plan to implement competing risks models on public health studies.
PubDate: 2022-03-01

• New closed-form estimator and its properties

Abstract: Abstract There is no closed form maximum likelihood estimator (MLE) for some distributions. This might cause some problems in real-time processing. Using an extension of Box–Cox transformation, we develop a closed-form estimator for the family of distributions. If such closed-form estimators exist, they have the invariance property like MLE and are equal in distribution with respect to the transformation. Specifically, the joint exact and asymptotic distributions of the closed-form estimators are the same irrespective of the transformation parameter, which is useful for statistical inference. For the gamma related and weighted Lindley related distributions, the closed-form estimators achieve strong consistency and asymptotic normality similar to MLE. That is, the closed-form estimators from the family of distributions obtained from an extension of the Box–Cox transformation for the gamma and weighted Lindley distributions as the initial distributions achieve strong consistency and asymptotic normality. A bias-corrected closed-form estimator that is also independent of the transformation is derived. In this sense, the closed-form estimator and the bias-corrected closed-form estimator are invariant with respect to the transformation. Some examples are provided to demonstrate the underlying theory. Some simulation studies and a real data example for the inverse gamma distribution are presented to illustrate the performance of the proposed estimators in this study.
PubDate: 2022-03-01

• Efficient information-based criteria for model selection in quantile
regression

Abstract: Abstract Information-based model selection criteria such as the AIC and BIC employ check loss functions to measure the goodness of fit for quantile regression models. Model selection using a check loss function is robust due to its resistance to outlying observations. In the present study, we suggest modifying the check loss function to achieve a more efficient goodness of fit. Because the cusp of the check loss is quadratically adjusted in the modified version, greater efficiency (or variance reduction) in the model selection is expected. Because we focus on model selection here, we do not modify the model-fitting process. Generalized cross-validation is another common method for choosing smoothing parameters in quantile smoothing splines. We describe how this can be adjusted using the modified check loss to increase efficiency. The proposed generalized cross-validation is designed to reflect the target quantile and sample size. Two real data sets and simulation studies are presented to evaluate its performance using linear and nonlinear quantile regression models.
PubDate: 2022-03-01

• Ensemble binary segmentation for irregularly spaced data with
change-points

Abstract: Abstract We propose a new technique for consistent estimation of the number and locations of the change-points in the structure of an irregularly spaced time series. The core of the segmentation procedure is the ensemble binary segmentation method (EBS), a technique in which a large number of multiple change-point detection tasks using the binary segmentation method are applied on sub-samples of the data of differing lengths, and then the results are combined to create an overall answer. We do not restrict the total number of change-points a time series can have, therefore, our proposed method works well when the spacings between change-points are short. Our main change-point detection statistic is the time-varying autoregressive conditional duration model on which we apply a transformation process in order to decorrelate it. To examine the performance of EBS we provide a simulation study for various types of scenarios. A proof of consistency is also provided. Our methodology is implemented in the R package eNchange, available to download from CRAN.
PubDate: 2022-03-01

• Fourth moment bound and stationary Gaussian processes with positive
correlation

Abstract: Abstract We develop a new technique for the proof of the fourth moment theorem on Wiener chaos to derive the bound in normal approximation of a random variable living a finite sum of Wiener chaos of a stationary Gaussian process with a positive correlaton. Thanks to newly developed techniques, an improved upper bound, expressed in terms of the fourth moment, will be obtained, compared with the one in Es-Sebaiy and Viens (Stoch Proc Appl 129:3018–3054, 2019). Our approach will be applied to the case where a random variable of functionals of Gaussian fields has a form of a power variation of a fractional Brownain motion and a polynomial variation of a fractional stationary Ornstein-Uhlenbeck process.
PubDate: 2022-03-01

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