Journal of Statistical Planning and Inference最新文献_第10页

Regression analysis of longitudinal data with mixed synchronous and asynchronous longitudinal covariates 使用混合同步和非同步纵向协变量对纵向数据进行回归分析

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2023-12-09 DOI: 10.1016/j.jspi.2023.106135

Zhuowei Sun , Hongyuan Cao , Li Chen , Jason P. Fine

引用次数: 0

Maximum correntropy criterion regression models with tending-to-zero scale parameters 尺度参数趋于零的最大熵标准回归模型

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2023-12-09 DOI: 10.1016/j.jspi.2023.106134

Lianqiang Yang , Ying Jing , Teng Li

引用次数: 0

Resampling techniques for a class of smooth, possibly data-adaptive empirical copulas 一类平滑、可能具有数据适应性的经验共存系数的重采样技术

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2023-12-07 DOI: 10.1016/j.jspi.2023.106132

Ivan Kojadinovic , Bingqing Yi

{"title":"Resampling techniques for a class of smooth, possibly data-adaptive empirical copulas","authors":"Ivan Kojadinovic , Bingqing Yi","doi":"10.1016/j.jspi.2023.106132","DOIUrl":"10.1016/j.jspi.2023.106132","url":null,"abstract":"<div>We investigate the validity of two resampling techniques when carrying out inference on the underlying unknown copula using a recently proposed class of smooth, possibly data-adaptive nonparametric estimators that contains empirical Bernstein copulas (and thus the empirical beta copula). Following Kiriliouk et al. (2021), the first resampling technique is based on drawing samples from the smooth estimator and can only can be used in the case of independent observations. The second technique is a smooth extension of the so-called sequential dependent multiplier bootstrap and can thus be used in a time series setting and, possibly, for change-point analysis. The two studied resampling schemes are applied to confidence interval construction and the offline detection of changes in the cross-sectional dependence of multivariate time series, respectively. Monte Carlo experiments confirm the possible advantages of such smooth inference procedures over their non-smooth counterparts. A by-product of this work is the study of the weak consistency and finite-sample performance of two classes of smooth estimators of the first-order partial derivatives of a copula which can have applications in mean and quantile regression.</div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"231 ","pages":"Article 106132"},"PeriodicalIF":0.9,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138554564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A global test for heteroscedastic one-way FMANOVA with applications 异方差单向 FMANOVA 全局检验及其应用

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2023-12-05 DOI: 10.1016/j.jspi.2023.106133

Tianming Zhu , Jin-Ting Zhang , Ming-Yen Cheng

{"title":"A global test for heteroscedastic one-way FMANOVA with applications","authors":"Tianming Zhu , Jin-Ting Zhang , Ming-Yen Cheng","doi":"10.1016/j.jspi.2023.106133","DOIUrl":"https://doi.org/10.1016/j.jspi.2023.106133","url":null,"abstract":"<div>Multivariate functional data are prevalent in various fields such as biology, climatology, and finance. Motivated by the World Health Data applications, in this study, we propose and examine a global test for assessing the equality of multiple mean functions in multivariate functional data. This test addresses the one-way Functional Multivariate Analysis of Variance (FMANOVA) problem, which is a fundamental issue in the analysis of multivariate functional data. While numerous analysis of variance tests have been proposed and studied for univariate functional data, only a limited number of methods have been developed for the one-way FMANOVA problem. Furthermore, our global test has the ability to handle heteroscedasticity in the unknown covariance function matrices that underlie the multivariate functional data, which is not possible with existing methods. We establish the asymptotic null distribution of the test statistic as a chi-squared-type mixture, which depends on the eigenvalues of the covariance function matrices. To approximate the null distribution, we introduce a Welch–Satterthwaite type chi-squared-approximation with consistent parameter estimation. The proposed test exhibits root-<math><mi>n</mi></math> consistency, meaning it possesses nontrivial power against a local alternative. Additionally, it offers superior computational efficiency compared to several permutation-based tests. Through simulation studies and applications to the World Health Data, we highlight the advantages of our global test.</div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"231 ","pages":"Article 106133"},"PeriodicalIF":0.9,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138490271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Inference on regression model with misclassified binary response 二元响应分类错误的回归模型推理

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2023-11-29 DOI: 10.1016/j.jspi.2023.106121

Arindam Chatterjee , Tathagata Bandyopadhyay , Ayoushman Bhattacharya

{"title":"Inference on regression model with misclassified binary response","authors":"Arindam Chatterjee , Tathagata Bandyopadhyay , Ayoushman Bhattacharya","doi":"10.1016/j.jspi.2023.106121","DOIUrl":"https://doi.org/10.1016/j.jspi.2023.106121","url":null,"abstract":"<div>Misclassification of binary responses, if ignored, may severely bias the maximum likelihood estimators (MLEs) of regression parameters. For such data, a binary regression model incorporating non-differential classification errors is extensively used by researchers in different application contexts. We strongly caution against indiscriminate use of this model considering the fact that it suffers from a serious estimation problem due to confounding of the unknown misclassification probabilities with the regression parameters, and thus, may lead to a highly biased estimate. To overcome this problem, we propose here the use of an internal validation sample in addition to the main sample. Assuming differential classification errors, we consider MLEs of the regression parameters based on the joint likelihood of the main sample and the internal validation sample. We then develop a rigorous asymptotic theory for the joint MLEs under standard assumptions. To facilitate its easy implementation for inference, we propose a bootstrap approximation to the asymptotic distribution and prove its consistency. The results of the simulation studies suggest that even an extremely small validation sample may lead to a vastly improved inference. Finally, the methodology is illustrated with a real-life survey data.</div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"231 ","pages":"Article 106121"},"PeriodicalIF":0.9,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138465636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Construction of mixed-level screening designs using Hadamard matrices 使用Hadamard矩阵构建混合水平筛选设计

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2023-11-23 DOI: 10.1016/j.jspi.2023.106131

Bo Hu , Dongying Wang , Fasheng Sun

引用次数: 0

Mallows model averaging based on kernel regression imputation with responses missing at random 基于随机缺失响应核回归插值的Mallows模型平均

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2023-11-22 DOI: 10.1016/j.jspi.2023.106130

Hengkun Zhu, Guohua Zou

引用次数: 0

Jackknife empirical likelihood confidence intervals for the categorical Gini correlation 分类基尼相关的折刀经验似然置信区间

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2023-11-20 DOI: 10.1016/j.jspi.2023.106123

Sameera Hewage, Yongli Sang

{"title":"Jackknife empirical likelihood confidence intervals for the categorical Gini correlation","authors":"Sameera Hewage, Yongli Sang","doi":"10.1016/j.jspi.2023.106123","DOIUrl":"https://doi.org/10.1016/j.jspi.2023.106123","url":null,"abstract":"<div>The categorical Gini correlation, <math><msub><mrow><mi>ρ</mi></mrow><mrow><mi>g</mi></mrow></msub></math>, was proposed by Dang et al. (2021) to measure the dependence between a categorical variable, <math><mi>Y</mi></math>, and a numerical variable, <math><mi>X</mi></math>. It has been shown that <math><msub><mrow><mi>ρ</mi></mrow><mrow><mi>g</mi></mrow></msub></math> has more appealing properties than current existing dependence measurements. In this paper, we develop the jackknife empirical likelihood (JEL) method for <math><msub><mrow><mi>ρ</mi></mrow><mrow><mi>g</mi></mrow></msub></math>. Confidence intervals for the Gini correlation are constructed without estimating the asymptotic variance. Adjusted and weighted JEL are explored to improve the performance of the standard JEL. Simulation studies show that our methods are competitive to existing methods in terms of coverage accuracy and shortness of confidence intervals. The proposed methods are illustrated in an application on two real datasets.</div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"231 ","pages":"Article 106123"},"PeriodicalIF":0.9,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138430387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multidimensional objective prior distribution from a scoring rule 基于评分规则的多维客观先验分布

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2023-11-18 DOI: 10.1016/j.jspi.2023.106122

Isadora Antoniano-Villalobos , Cristiano Villa , Stephen G. Walker

引用次数: 0

Subgroup analysis for the functional linear model 功能线性模型的亚群分析

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2023-11-15 DOI: 10.1016/j.jspi.2023.106120

Yifan Sun , Ziyi Liu , Wu Wang

引用次数: 0