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

Abstract: Abstract In this paper, we propose a control chart to monitor multivariate data with missing values as an alternative to the most common imputation-based control chart. The chart statistic we use is the weighted sum of the chi-square statistics of each variable, say $$Q^*$$ , which is named as pooled component test statistic (PCT) by Wu et al. (2006). We modify the statistic in the context of monitoring and approximate the in-control distribution of $$Q^*$$ as a scaled chi-square distribution. The PCT chart we propose in this paper is a Shewhart chart using $$Q^*$$ , and its control limits are from the estimate of the approximate in-control distribution of $$Q^*$$ . We numerically show that the PCT chart performs better than the imputation-based methods in the literature. We finally apply it to monitoring a semiconductor manufacturing process using production environmental variables.
PubDate: 2022-08-05

• A note on maximum likelihood estimation for mixture models

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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