{"title":"应用于分类的 NMAR 响应变量核型回归估计器","authors":"Majid Mojirsheibani, Arin Khudaverdyan","doi":"10.1016/j.spl.2024.110246","DOIUrl":null,"url":null,"abstract":"<div><p>This work deals with the problem of nonparametric estimation of a regression function when the response variable may be missing according to a <em>not-missing-at-random</em> (NMAR) setup. To assess the theoretical performance of our estimators, we study their strong convergence properties in <span><math><msub><mrow><mi>L</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span> norms where we also look into their rates of convergence. We also study applications of our results to the problem of statistical classification in semi-supervised learning.</p></div>","PeriodicalId":0,"journal":{"name":"","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167715224002153/pdfft?md5=94e4730a876fb2ff6ba620c03294cfcb&pid=1-s2.0-S0167715224002153-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A kernel-type regression estimator for NMAR response variables with applications to classification\",\"authors\":\"Majid Mojirsheibani, Arin Khudaverdyan\",\"doi\":\"10.1016/j.spl.2024.110246\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This work deals with the problem of nonparametric estimation of a regression function when the response variable may be missing according to a <em>not-missing-at-random</em> (NMAR) setup. To assess the theoretical performance of our estimators, we study their strong convergence properties in <span><math><msub><mrow><mi>L</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span> norms where we also look into their rates of convergence. We also study applications of our results to the problem of statistical classification in semi-supervised learning.</p></div>\",\"PeriodicalId\":0,\"journal\":{\"name\":\"\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0,\"publicationDate\":\"2024-08-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0167715224002153/pdfft?md5=94e4730a876fb2ff6ba620c03294cfcb&pid=1-s2.0-S0167715224002153-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0167715224002153\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167715224002153","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A kernel-type regression estimator for NMAR response variables with applications to classification
This work deals with the problem of nonparametric estimation of a regression function when the response variable may be missing according to a not-missing-at-random (NMAR) setup. To assess the theoretical performance of our estimators, we study their strong convergence properties in norms where we also look into their rates of convergence. We also study applications of our results to the problem of statistical classification in semi-supervised learning.
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