{"title":"Fused inverse regression with multi-dimensional responses","authors":"Youyoung Cho, Hyoseon Hana, J. Yoo","doi":"10.29220/CSAM.2021.28.3.267","DOIUrl":null,"url":null,"abstract":"A regression with multi-dimensional responses is quite common nowadays in the so-called big data era. In such regression, to relieve the curse of dimension due to high-dimension of responses, the dimension reduction of predictors is essential in analysis. Su ffi cient dimension reduction provides e ff ective tools for the reduction, but there are few su ffi cient dimension reduction methodologies for multivariate regression. To fill this gap, we newly propose two fused slice-based inverse regression methods. The proposed approaches are robust to the numbers of clusters or slices and improve the estimation results over existing methods by fusing many kernel matrices. Numerical studies are presented and are compared with existing methods. Real data analysis confirms practical usefulness of the proposed methods.","PeriodicalId":44931,"journal":{"name":"Communications for Statistical Applications and Methods","volume":" ","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2021-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communications for Statistical Applications and Methods","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.29220/CSAM.2021.28.3.267","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0
Abstract
A regression with multi-dimensional responses is quite common nowadays in the so-called big data era. In such regression, to relieve the curse of dimension due to high-dimension of responses, the dimension reduction of predictors is essential in analysis. Su ffi cient dimension reduction provides e ff ective tools for the reduction, but there are few su ffi cient dimension reduction methodologies for multivariate regression. To fill this gap, we newly propose two fused slice-based inverse regression methods. The proposed approaches are robust to the numbers of clusters or slices and improve the estimation results over existing methods by fusing many kernel matrices. Numerical studies are presented and are compared with existing methods. Real data analysis confirms practical usefulness of the proposed methods.
期刊介绍:
Communications for Statistical Applications and Methods (Commun. Stat. Appl. Methods, CSAM) is an official journal of the Korean Statistical Society and Korean International Statistical Society. It is an international and Open Access journal dedicated to publishing peer-reviewed, high quality and innovative statistical research. CSAM publishes articles on applied and methodological research in the areas of statistics and probability. It features rapid publication and broad coverage of statistical applications and methods. It welcomes papers on novel applications of statistical methodology in the areas including medicine (pharmaceutical, biotechnology, medical device), business, management, economics, ecology, education, computing, engineering, operational research, biology, sociology and earth science, but papers from other areas are also considered.