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Journal of the Korean Statistical Society最新文献

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Penalized empirical likelihood for longitudinal expectile regression with growing dimensional data 纵向期望值回归的惩罚性经验似然法与维度不断增加的数据
IF 0.6 4区 数学
Journal of the Korean Statistical Society Pub Date : 2024-04-09 DOI: 10.1007/s42952-024-00265-4
Ting Zhang, Yanan Wang, Lei Wang
{"title":"Penalized empirical likelihood for longitudinal expectile regression with growing dimensional data","authors":"Ting Zhang, Yanan Wang, Lei Wang","doi":"10.1007/s42952-024-00265-4","DOIUrl":"https://doi.org/10.1007/s42952-024-00265-4","url":null,"abstract":"<p>Expectile regression (ER) naturally extends the classical least squares to investigate heterogeneous effects of covariates on the distribution of the response variable. In this paper, we propose a penalized empirical likelihood (PEL) based ER estimator, which incorporates quadratic inference function and generalized estimating equation to construct the PEL procedure for longitudinal data. We investigate the asymptotic properties of the PEL estimator when the number of covariates is allowed to diverge as the sample size increases. The finite-sample performance of the proposed estimator is studied through simulations, and an application to yeast cell-cycle gene expression data is also presented.</p>","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140577264","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 trend-free optimal block designs under some correlation structures 在某些相关结构下构建无趋势最优区块设计
IF 0.6 4区 数学
Journal of the Korean Statistical Society Pub Date : 2024-04-04 DOI: 10.1007/s42952-024-00264-5
Akram Fakhari-Esferizi, Razieh Khodsiani
{"title":"Construction of trend-free optimal block designs under some correlation structures","authors":"Akram Fakhari-Esferizi, Razieh Khodsiani","doi":"10.1007/s42952-024-00264-5","DOIUrl":"https://doi.org/10.1007/s42952-024-00264-5","url":null,"abstract":"<p>In many block experiments where the treatments are applied to the experimental units sequentially over time or space, there may be a systematic trend effect that influences the observations in addition to the block and the treatment effects. In some previous literature, optimality of block designs under trend effects has been studied in the case of uncorrelated observations when the number of treatments is greater than the size of blocks. This article deals with the block model incorporating trend components when the observations are correlated under some correlation structures. We introduce methods for constructing trend-free optimal designs with every treatment number and block size.</p>","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140577110","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
Double data piling: a high-dimensional solution for asymptotically perfect multi-category classification 双重数据堆积：渐近完美多类别分类的高维解决方案
IF 0.6 4区 数学
Journal of the Korean Statistical Society Pub Date : 2024-04-03 DOI: 10.1007/s42952-024-00263-6
Taehyun Kim, Woonyoung Chang, Jeongyoun Ahn, Sungkyu Jung
{"title":"Double data piling: a high-dimensional solution for asymptotically perfect multi-category classification","authors":"Taehyun Kim, Woonyoung Chang, Jeongyoun Ahn, Sungkyu Jung","doi":"10.1007/s42952-024-00263-6","DOIUrl":"https://doi.org/10.1007/s42952-024-00263-6","url":null,"abstract":"<p>For high-dimensional classification, interpolation of training data manifests as the data piling phenomenon, in which linear projections of data vectors from each class collapse to a single value. Recent research has revealed an additional phenomenon known as the ‘second data piling’ for independent test data in binary classification, providing a theoretical understanding of asymptotically perfect classification. This paper extends these findings to multi-category classification and provides a comprehensive characterization of the double data piling phenomenon. We define the maximal data piling subspace, which maximizes the sum of pairwise distances between piles of training data in multi-category classification. Furthermore, we show that a second data piling subspace that induces data piling for independent data exists and can be consistently estimated by projecting the negatively-ridged discriminant subspace onto an estimated ‘signal’ subspace. By leveraging this second data piling phenomenon, we propose a bias-correction strategy for class assignments, which asymptotically achieves perfect classification. The present research sheds light on benign overfitting and enhances the understanding of perfect multi-category classification of high-dimensional discrimination with a help of high-dimensional asymptotics.</p>","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140577260","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
Correction: Disseminating massive frequency tables by masking aggregated cell frequencies 更正：通过掩盖汇总的小区频率来传播海量频率表
IF 0.6 4区 数学
Journal of the Korean Statistical Society Pub Date : 2024-04-03 DOI: 10.1007/s42952-024-00267-2
Min-Jeong Park, Hang J. Kim, Sunghoon Kwon
{"title":"Correction: Disseminating massive frequency tables by masking aggregated cell frequencies","authors":"Min-Jeong Park, Hang J. Kim, Sunghoon Kwon","doi":"10.1007/s42952-024-00267-2","DOIUrl":"https://doi.org/10.1007/s42952-024-00267-2","url":null,"abstract":"","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140749866","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
Nonparametric tests for combined location-scale and Lehmann alternatives using adaptive approach and max-type metric 利用自适应方法和最大类型度量对位置尺度和莱曼综合替代方案进行非参数检验
IF 0.6 4区 数学
Journal of the Korean Statistical Society Pub Date : 2024-04-02 DOI: 10.1007/s42952-024-00262-7
{"title":"Nonparametric tests for combined location-scale and Lehmann alternatives using adaptive approach and max-type metric","authors":"","doi":"10.1007/s42952-024-00262-7","DOIUrl":"https://doi.org/10.1007/s42952-024-00262-7","url":null,"abstract":"<h3>Abstract</h3> <p>The paper deals with the classical two-sample problem for the combined location-scale and Lehmann alternatives, known as the versatile alternative. Recently, a combination of the square of the standardized Wilcoxon, the standardized Ansari–Bradley and the standardized Anti-Savage statistics based on the Euclidean distance has been proposed. The Anti-Savage test is the locally most powerful rank test for the right-skewed Gumbel distribution. Furthermore, the Savage test is the locally most powerful linear rank test for the left-skewed Gumbel distribution. Then, a test statistic combining the Wilcoxon, the Ansari–Bradley, and Savage statistics is proposed. The limiting distribution of the proposed statistic is derived under the null and the alternative hypotheses. In addition, the asymptotic power of the suggested statistic is investigated. Moreover, an adaptive test is proposed based on a selection rule. We compare the power performance against various fixed alternatives using Monte Carlo. The proposed test statistic displays outstanding performance in certain situations. An illustration of the proposed test statistic is presented to explain a biomedical experiment. Finally, we offer some concluding remarks.</p>","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140577293","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
Variants of non-symmetric correspondence analysis for nominal and ordinal variables 名义变量和序数变量非对称对应分析的变体
IF 0.6 4区 数学
Journal of the Korean Statistical Society Pub Date : 2024-03-23 DOI: 10.1007/s42952-023-00253-0
Riya R. Jain, Kirtee K. Kamalja
{"title":"Variants of non-symmetric correspondence analysis for nominal and ordinal variables","authors":"Riya R. Jain, Kirtee K. Kamalja","doi":"10.1007/s42952-023-00253-0","DOIUrl":"https://doi.org/10.1007/s42952-023-00253-0","url":null,"abstract":"<p>Non-symmetric correspondence analysis (NSCA) is a multivariate data analysis technique that has gained increasing attention in recent years. NSCA is an extension of traditional correspondence analysis that allows for the analysis of asymmetric association between two or more categorical variables. NSCA involves graphically depicting the one-way relationship between variables cross classified in a contingency table through a biplot. This paper provides a comprehensive overview of the popular approaches of NSCA developed over the years. Some fundamental variations in the family of NSCA such as Simple NSCA, Doubly Ordered NSCA, Singly Ordered NSCA, Three-way Nominal NSCA, Triply Ordered NSCA etc. are discussed thoroughly. A systematic step-by-step algorithms for each variant of NSCA and their demonstrations are neatly presented. Further a summary of NSCA variants in literature, the concise tabular presentation of R-packages developed for variants of CA/NSCA and a collection of variety of datasets where NSCA is performed are the key features of the paper. Moreover, we compare and contrast the method of NSCA with multinomial logistic regression (MNLR) to discuss some disparities between both the approaches. The paper aims to provide the theoretical, practical and computational issues of NSCA in structured manner and to highlight the further challenges with reference to NSCA.</p>","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140201070","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
Finding an NARE whose minimal nonnegative solution represents first passage quantities in the two-dimensional Brownian motion 寻找一个 NARE，其最小非负解代表二维布朗运动中的第一通道量
IF 0.6 4区 数学
Journal of the Korean Statistical Society Pub Date : 2024-03-22 DOI: 10.1007/s42952-024-00261-8
Sung-Chul Hong, Soohan Ahn
{"title":"Finding an NARE whose minimal nonnegative solution represents first passage quantities in the two-dimensional Brownian motion","authors":"Sung-Chul Hong, Soohan Ahn","doi":"10.1007/s42952-024-00261-8","DOIUrl":"https://doi.org/10.1007/s42952-024-00261-8","url":null,"abstract":"<p>The goal of this paper is to find a nonsymmetric algebraic Riccati equation(NARE) of which the minimal nonnegative solution can represent the Laplace transform of the total increment of one component during the first passage time of the other in the two-dimensional Brownian motion. For that purpose, we construct a sequence of two-dimensional Markov modulated fluid flow which converges to the two-dimensional Brownian motion and then derive various approximation results relevant to the NARE of our interest. This is the preliminary research for investigating first-passage-related quantities in the two-dimensional Markov modulated Brownian motion in which the parameters vary according to the states of an underlying Markov process.</p>","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140201065","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
Two-sample test of stochastic block models via the maximum sampling entry-wise deviation 通过最大抽样条目偏差对随机块模型进行双样本检验
IF 0.6 4区 数学
Journal of the Korean Statistical Society Pub Date : 2024-03-03 DOI: 10.1007/s42952-024-00260-9
Qianyong Wu, Jiang Hu
{"title":"Two-sample test of stochastic block models via the maximum sampling entry-wise deviation","authors":"Qianyong Wu, Jiang Hu","doi":"10.1007/s42952-024-00260-9","DOIUrl":"https://doi.org/10.1007/s42952-024-00260-9","url":null,"abstract":"<p>The paper discusses a statistical problem related to testing for differences between two networks with community structures. While existing methods have been proposed, they encounter challenges and do not perform effectively when the networks become sparse. We propose a test statistic that combines a method proposed by Wu and Hu (2024) and a resampling process. Specifically, the proposed test statistic proves effective under the condition that the community-wise edge probability matrices have entries of order <span>(Omega (log n/n))</span>, where <i>n</i> denotes the network size. We derive the asymptotic null distribution of the test statistic and provide a guarantee of asymptotic power against the alternative hypothesis. To evaluate the performance of the proposed test statistic, we conduct simulations and provide real data examples. The results indicate that the proposed test statistic performs well for both dense and sparse networks.</p>","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140019370","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
Jackknife model averaging for linear regression models with missing responses 对有缺失响应的线性回归模型进行积刀模型平均化
IF 0.6 4区 数学
Journal of the Korean Statistical Society Pub Date : 2024-02-19 DOI: 10.1007/s42952-024-00259-2
Jie Zeng, Weihu Cheng, Guozhi Hu
{"title":"Jackknife model averaging for linear regression models with missing responses","authors":"Jie Zeng, Weihu Cheng, Guozhi Hu","doi":"10.1007/s42952-024-00259-2","DOIUrl":"https://doi.org/10.1007/s42952-024-00259-2","url":null,"abstract":"<p>We consider model averaging estimation problem in the linear regression model with missing response data, that allows for model misspecification. Based on the ‘complete’ data set for the response variable after inverse propensity score weighted imputation, we construct a leave-one-out cross-validation criterion for allocating model weights, where the propensity score model is estimated by the covariate balancing propensity score method. We derive some theoretical results to justify the proposed strategy. Firstly, when all candidate outcome regression models are misspecified, our procedures are proved to achieve optimality in terms of asymptotically minimizing the squared loss. Secondly, when the true outcome regression model is among the set of candidate models, the resulting model averaging estimators of the regression parameters are shown to be root-<i>n</i> consistent. Simulation studies provide evidence of the superiority of our methods over other existing model averaging methods, even when the propensity score model is misspecified. As an illustration, the approach is further applied to study the CD4 data.</p>","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139904122","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 self-normalization test for structural breaks in a regression model for panel data sets 面板数据集回归模型结构断裂的自归一化检验
IF 0.6 4区 数学
Journal of the Korean Statistical Society Pub Date : 2024-02-15 DOI: 10.1007/s42952-024-00255-6
Ji-Eun Choi, Dong Wan Shin
{"title":"A self-normalization test for structural breaks in a regression model for panel data sets","authors":"Ji-Eun Choi, Dong Wan Shin","doi":"10.1007/s42952-024-00255-6","DOIUrl":"https://doi.org/10.1007/s42952-024-00255-6","url":null,"abstract":"<p>We construct a new structural break test in a panel regression model using the self-normalization method. The self-normalization test is shown to be superior to an existing test in that the former is theoretically and experimentally valid for regression models with serially and/or cross-sectionally correlated errors while the latter is not. We derive the asymptotic null distribution of the self-normalization test and its consistency under an alternative hypothesis. Unlike the existing test requiring bootstrap computation for critical values, the self-normalization test is implemented easily with a set of simple critical values. A Monte Carlo experiment reports that the self-normalization resolves the severe over-size problem of the existing test under serial and/or cross-sectional error correlation.</p>","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139767824","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
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