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Journal of the Korean Statistical Society最新文献

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Classification of repeated measurements using bias corrected Euclidean distance discriminant function 利用偏差校正欧氏距离判别函数对重复测量进行分类
IF 0.6 4区 数学
Journal of the Korean Statistical Society Pub Date : 2023-12-12 DOI: 10.1007/s42952-023-00246-z
Edward Kanuti Ngailo, Saralees Nadarajah
{"title":"Classification of repeated measurements using bias corrected Euclidean distance discriminant function","authors":"Edward Kanuti Ngailo, Saralees Nadarajah","doi":"10.1007/s42952-023-00246-z","DOIUrl":"https://doi.org/10.1007/s42952-023-00246-z","url":null,"abstract":"<p>This paper introduces a novel approach for approximating misclassification probabilities in Euclidean distance classifier when the group means exhibit a bilinear structure such as in the growth curve model first proposed by Potthoff and Roy (Biometrika 51:313–326, 1964). Initially, by leveraging certain statistical relationships, we establish two general results for the improved Euclidean discriminant function in both weighted and unweighted growth curve mean structures. We derive these approximations for the expected misclassification probabilities with respect to the distribution of the improved Euclidean discriminant function. Additionally, we compare the misclassification probabilities of the improved Euclidean discriminant function, the standard Euclidean discriminant function, and the linear discriminant function. It is important to note that in cases where the mean structure is weighted, a higher number of repeated measurements yields better classification results with the improved Euclidean discriminant function and the standard Euclidean discriminant function, allowing for more information to be acquired, as opposed to the linear discriminant function, which performs well with a smaller number of repeated measurements. Furthermore, we evaluate the accuracy of the suggested approximations by Monte Carlo simulations.</p>","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":"13 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138575049","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
Sparse functional linear models via calibrated concave-convex procedure 稀疏函数线性模型通过校准凹-凸程序
IF 0.6 4区 数学
Journal of the Korean Statistical Society Pub Date : 2023-12-03 DOI: 10.1007/s42952-023-00242-3
Young Joo Lee, Yongho Jeon
{"title":"Sparse functional linear models via calibrated concave-convex procedure","authors":"Young Joo Lee, Yongho Jeon","doi":"10.1007/s42952-023-00242-3","DOIUrl":"https://doi.org/10.1007/s42952-023-00242-3","url":null,"abstract":"<p>In this paper, we propose a calibrated ConCave-Convex Procedure (CCCP) for variable selection in high-dimensional functional linear models. The calibrated CCCP approach for the Smoothly Clipped Absolute Deviation (SCAD) penalty is known to produce a consistent solution path with probability converging to one in linear models. We incorporate the SCAD penalty into function-on-scalar regression models and phrase them as a type of group-penalized estimation using a basis expansion approach. We then implement the calibrated CCCP method to solve the nonconvex group-penalized problem. For the tuning procedure, we use the Extended Bayesian Information Criterion (EBIC) to ensure consistency in high-dimensional settings. In simulation studies, we compare the performance of the proposed method with two existing convex-penalized estimators in terms of variable selection consistency and prediction accuracy. Lastly, we apply the method to the gene expression dataset for sparsely estimating the time-varying effects of transcription factors on the regulation of yeast cell cycle genes.</p>","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":"25 7","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138496033","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 longitudinal regression model to analyze shape data using the Procrustes rotation 利用非参数纵向回归模型分析形状数据的Procrustes旋转
IF 0.6 4区 数学
Journal of the Korean Statistical Society Pub Date : 2023-12-03 DOI: 10.1007/s42952-023-00241-4
Meisam Moghimbeygi, Mousa Golalizadeh
{"title":"Nonparametric longitudinal regression model to analyze shape data using the Procrustes rotation","authors":"Meisam Moghimbeygi, Mousa Golalizadeh","doi":"10.1007/s42952-023-00241-4","DOIUrl":"https://doi.org/10.1007/s42952-023-00241-4","url":null,"abstract":"<p>Shape, as an intrinsic concept, can be considered as a source of information in some statistical analysis contexts. For instance, one of the important topics in morphology is to study the shape changes along time. From a topological viewpoint, shape data are points on a particular manifold and so to construct a longitudinal model for treating shape variation is not as trivial as thought. Unlike using the common parametric models to do such a task, we invoke Procrustes analysis in the context of a nonparametric framework and propose a simple, yet useful, model to deal with shape changes. After conveying the problem into the nonparametric regression model, we utilize the weighted least squares method to estimates the related parameters. Also, we illustrate implementing this new model in simulation studies and analyzing two biological data sets. Our proposed model shows its superiority while compared with other counterpart models.</p>","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":"25 6","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138496034","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
Variable selection for semiparametric accelerated failure time models with nonignorable missing data 具有不可忽略缺失数据的半参数加速失效时间模型的变量选择
IF 0.6 4区 数学
Journal of the Korean Statistical Society Pub Date : 2023-11-19 DOI: 10.1007/s42952-023-00238-z
Tianqing Liu, Xiaohui Yuan, Liuquan Sun
{"title":"Variable selection for semiparametric accelerated failure time models with nonignorable missing data","authors":"Tianqing Liu, Xiaohui Yuan, Liuquan Sun","doi":"10.1007/s42952-023-00238-z","DOIUrl":"https://doi.org/10.1007/s42952-023-00238-z","url":null,"abstract":"<p>The regularization approach for variable selection was well developed for semiparametric accelerated failure time (AFT) models, where the response variable is right censored. In the presence of missing data, this approach needs to be tailored to different missing data mechanisms. In this paper, we propose a flexible and generally applicable missing data mechanism for AFT models, which contains both ignorable and nonignorable missing data mechanism assumptions. We propose weighted rank (WR) estimators and corresponding penalized estimators of regression parameters under this missing data mechanism. An advantage of the WR estimators and corresponding penalized estimators is that they do not require specifying a missing data model for the proposed missing data mechanism. The theoretical properties of the WR and corresponding penalized estimators are established. Comprehensive simulation studies and a real data application further demonstrate the merits of our approach.</p>","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":"26 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138496032","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
Robust and Efficient derivative estimation under correlated errors 相关误差下稳健高效的导数估计
IF 0.6 4区 数学
Journal of the Korean Statistical Society Pub Date : 2023-11-18 DOI: 10.1007/s42952-023-00240-5
Deru Kong, Wei Shen, Shengli Zhao, WenWu Wang
{"title":"Robust and Efficient derivative estimation under correlated errors","authors":"Deru Kong, Wei Shen, Shengli Zhao, WenWu Wang","doi":"10.1007/s42952-023-00240-5","DOIUrl":"https://doi.org/10.1007/s42952-023-00240-5","url":null,"abstract":"<p>In real applications, the correlated data are commonly encountered. To model such data, many techniques have been proposed. However, of the developed techniques, emphasis has been on the mean function estimation under correlated errors, with scant attention paid to the derivative estimation. In this paper, we propose the locally weighted least squares regression based on different difference quotients to estimate the different order derivatives under correlated errors. For the proposed estimators, we derive their asymptotic bias and variance with different covariance structure errors, which dramatically reduce the estimation variance compared with traditional methods. Furthermore, we establish their asymptotic normality for constructing confidence interval. Based on the asymptotic mean integrated squared error, we provide a data-driven tuning parameters selection criterion. Simulation studies show that the proposed method is more robust and efficient than four other popular methods. Finally, we illustrate the usefulness of the proposed method with a real data example.</p>","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":"26 3","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138496031","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
Asymptotic bias of the $$ell _2$$-regularized error variance estimator $$ell _2$$ -正则化误差方差估计量的渐近偏差
4区 数学
Journal of the Korean Statistical Society Pub Date : 2023-11-14 DOI: 10.1007/s42952-023-00239-y
Semin Choi, Gunwoong Park
{"title":"Asymptotic bias of the $$ell _2$$-regularized error variance estimator","authors":"Semin Choi, Gunwoong Park","doi":"10.1007/s42952-023-00239-y","DOIUrl":"https://doi.org/10.1007/s42952-023-00239-y","url":null,"abstract":"","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":"12 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134954534","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 review on concomitants of order statistics and its application in parameter estimation under ranked set sampling 序统计量的伴随量及其在排序集抽样参数估计中的应用综述
4区 数学
Journal of the Korean Statistical Society Pub Date : 2023-11-13 DOI: 10.1007/s42952-023-00235-2
Rohan D. Koshti, Kirtee K. Kamalja
{"title":"A review on concomitants of order statistics and its application in parameter estimation under ranked set sampling","authors":"Rohan D. Koshti, Kirtee K. Kamalja","doi":"10.1007/s42952-023-00235-2","DOIUrl":"https://doi.org/10.1007/s42952-023-00235-2","url":null,"abstract":"","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":"63 31","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136282166","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 novel doubling-tripling-threshold accepting hybrid algorithm for constructing asymmetric space-filling designs 构造非对称空间填充设计的一种新的双-三阈值接受混合算法
4区 数学
Journal of the Korean Statistical Society Pub Date : 2023-11-03 DOI: 10.1007/s42952-023-00232-5
A. M. Elsawah
{"title":"A novel doubling-tripling-threshold accepting hybrid algorithm for constructing asymmetric space-filling designs","authors":"A. M. Elsawah","doi":"10.1007/s42952-023-00232-5","DOIUrl":"https://doi.org/10.1007/s42952-023-00232-5","url":null,"abstract":"","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":"11 34","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135818350","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
Variable selection for single-index models based on martingale difference divergence 基于鞅差分散度的单指标模型变量选择
4区 数学
Journal of the Korean Statistical Society Pub Date : 2023-10-25 DOI: 10.1007/s42952-023-00234-3
Xiaohui Yuan, Yue Wang, Yiming Wang, Tianqing Liu
{"title":"Variable selection for single-index models based on martingale difference divergence","authors":"Xiaohui Yuan, Yue Wang, Yiming Wang, Tianqing Liu","doi":"10.1007/s42952-023-00234-3","DOIUrl":"https://doi.org/10.1007/s42952-023-00234-3","url":null,"abstract":"","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134973791","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
Distributed smoothed rank regression with heterogeneous errors for massive data 海量数据异构误差的分布平滑秩回归
4区 数学
Journal of the Korean Statistical Society Pub Date : 2023-10-23 DOI: 10.1007/s42952-023-00237-0
Xiaohui Yuan, Xinran Zhang, Yue Wang, Chunjie Wang
{"title":"Distributed smoothed rank regression with heterogeneous errors for massive data","authors":"Xiaohui Yuan, Xinran Zhang, Yue Wang, Chunjie Wang","doi":"10.1007/s42952-023-00237-0","DOIUrl":"https://doi.org/10.1007/s42952-023-00237-0","url":null,"abstract":"","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":"18 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135368681","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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