{"title":"通过局部平均法为半变量系数模型选择变量","authors":"Xinyi Qi, Mengjie Liu, Chuanlong Xie, Heng Peng","doi":"10.1002/sta4.703","DOIUrl":null,"url":null,"abstract":"This study aims to provide novel insights into variable selection in the semivarying coefficient model. We focus on the problem of variable selection and screening for the constant coefficient part. A common approach in the existing literature is to infer the constant coefficients by transforming the problem into a linear model scenario, utilizing a fine estimator of the varying coefficients. In this paper, we propose an approximation method for the varying coefficient functions using local averaging, which is characterized by its simplicity, rough and computational efficiency. Additionally, we introduce an adaptive lasso estimator and a forward regression algorithm specifically designed for semivarying coefficient models. Theoretical and experimental results highlight the effectiveness of the local averaging method in extending variable selection techniques from the linear model to the semivarying coefficient model. Our proposed approaches demonstrate a significant improvement in inference speed compared with baseline methods, with little loss of asymptotic efficiency.","PeriodicalId":56159,"journal":{"name":"Stat","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Variable selection for semivarying coefficient models via local averaging\",\"authors\":\"Xinyi Qi, Mengjie Liu, Chuanlong Xie, Heng Peng\",\"doi\":\"10.1002/sta4.703\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study aims to provide novel insights into variable selection in the semivarying coefficient model. We focus on the problem of variable selection and screening for the constant coefficient part. A common approach in the existing literature is to infer the constant coefficients by transforming the problem into a linear model scenario, utilizing a fine estimator of the varying coefficients. In this paper, we propose an approximation method for the varying coefficient functions using local averaging, which is characterized by its simplicity, rough and computational efficiency. Additionally, we introduce an adaptive lasso estimator and a forward regression algorithm specifically designed for semivarying coefficient models. Theoretical and experimental results highlight the effectiveness of the local averaging method in extending variable selection techniques from the linear model to the semivarying coefficient model. Our proposed approaches demonstrate a significant improvement in inference speed compared with baseline methods, with little loss of asymptotic efficiency.\",\"PeriodicalId\":56159,\"journal\":{\"name\":\"Stat\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Stat\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1002/sta4.703\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Stat","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1002/sta4.703","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
Variable selection for semivarying coefficient models via local averaging
This study aims to provide novel insights into variable selection in the semivarying coefficient model. We focus on the problem of variable selection and screening for the constant coefficient part. A common approach in the existing literature is to infer the constant coefficients by transforming the problem into a linear model scenario, utilizing a fine estimator of the varying coefficients. In this paper, we propose an approximation method for the varying coefficient functions using local averaging, which is characterized by its simplicity, rough and computational efficiency. Additionally, we introduce an adaptive lasso estimator and a forward regression algorithm specifically designed for semivarying coefficient models. Theoretical and experimental results highlight the effectiveness of the local averaging method in extending variable selection techniques from the linear model to the semivarying coefficient model. Our proposed approaches demonstrate a significant improvement in inference speed compared with baseline methods, with little loss of asymptotic efficiency.
StatDecision Sciences-Statistics, Probability and Uncertainty
CiteScore
1.10
自引率
0.00%
发文量
85
期刊介绍:
Stat is an innovative electronic journal for the rapid publication of novel and topical research results, publishing compact articles of the highest quality in all areas of statistical endeavour. Its purpose is to provide a means of rapid sharing of important new theoretical, methodological and applied research. Stat is a joint venture between the International Statistical Institute and Wiley-Blackwell.
Stat is characterised by:
• Speed - a high-quality review process that aims to reach a decision within 20 days of submission.
• Concision - a maximum article length of 10 pages of text, not including references.
• Supporting materials - inclusion of electronic supporting materials including graphs, video, software, data and images.
• Scope - addresses all areas of statistics and interdisciplinary areas.
Stat is a scientific journal for the international community of statisticians and researchers and practitioners in allied quantitative disciplines.