{"title":"Regression analysis of elliptically symmetric directional data","authors":"Zehao Yu, Xianzheng Huang","doi":"10.1016/j.csda.2025.108167","DOIUrl":null,"url":null,"abstract":"<div><div>A comprehensive toolkit is developed for regression analysis of directional data based on a flexible class of angular Gaussian distributions. Informative testing procedures to assess rotational symmetry around the mean direction, and the dependence of model parameters on covariates are proposed. Bootstrap-based algorithms are provided to assess the significance of the proposed test statistics. Moreover, a prediction region that achieves the smallest volume in a class of ellipsoidal prediction regions of the same coverage probability is constructed. The efficacy of these inference procedures is demonstrated in simulation experiments. Finally, this new toolkit is used to analyze directional data originating from a hydrology study and a bioinformatics application.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"208 ","pages":"Article 108167"},"PeriodicalIF":1.5000,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Statistics & Data Analysis","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S016794732500043X","RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
A comprehensive toolkit is developed for regression analysis of directional data based on a flexible class of angular Gaussian distributions. Informative testing procedures to assess rotational symmetry around the mean direction, and the dependence of model parameters on covariates are proposed. Bootstrap-based algorithms are provided to assess the significance of the proposed test statistics. Moreover, a prediction region that achieves the smallest volume in a class of ellipsoidal prediction regions of the same coverage probability is constructed. The efficacy of these inference procedures is demonstrated in simulation experiments. Finally, this new toolkit is used to analyze directional data originating from a hydrology study and a bioinformatics application.
期刊介绍:
Computational Statistics and Data Analysis (CSDA), an Official Publication of the network Computational and Methodological Statistics (CMStatistics) and of the International Association for Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis. The journal consists of four refereed sections which are divided into the following subject areas:
I) Computational Statistics - Manuscripts dealing with: 1) the explicit impact of computers on statistical methodology (e.g., Bayesian computing, bioinformatics,computer graphics, computer intensive inferential methods, data exploration, data mining, expert systems, heuristics, knowledge based systems, machine learning, neural networks, numerical and optimization methods, parallel computing, statistical databases, statistical systems), and 2) the development, evaluation and validation of statistical software and algorithms. Software and algorithms can be submitted with manuscripts and will be stored together with the online article.
II) Statistical Methodology for Data Analysis - Manuscripts dealing with novel and original data analytical strategies and methodologies applied in biostatistics (design and analytic methods for clinical trials, epidemiological studies, statistical genetics, or genetic/environmental interactions), chemometrics, classification, data exploration, density estimation, design of experiments, environmetrics, education, image analysis, marketing, model free data exploration, pattern recognition, psychometrics, statistical physics, image processing, robust procedures.
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III) Special Applications - [...]
IV) Annals of Statistical Data Science [...]