{"id":2565,"date":"2024-11-19T20:00:06","date_gmt":"2024-11-19T12:00:06","guid":{"rendered":"https:\/\/changjinyuan.com\/?p=2565"},"modified":"2024-12-25T17:13:50","modified_gmt":"2024-12-25T09:13:50","slug":"%e5%bc%a0%e4%bd%b3-chen-x-2019-robust-sufficient-dimension-reduction-via-ball-covariance-computational-statistics-data-analysis-vol-140-pp-144-154","status":"publish","type":"post","link":"https:\/\/changjinyuan.com\/index.php\/publications\/publications-all\/2565\/","title":{"rendered":"\u5f20\u4f73, &#038; Chen, X. (2019). Robust sufficient dimension reduction via ball covariance. Computational Statistics &#038; Data Analysis, 140, 144-154."},"content":{"rendered":"<p>Sufficient dimension reduction is an important branch of dimension reduction, which includes variable selection and projection methods. Most of the sufficient dimension reduction methods are sensitive to outliers and heavy-tailed predictors, and require strict restrictions on the predictors and the response. In order to widen the applicability of sufficient dimension reduction, we propose BCov-SDR, a novel sufficient dimension reduction approach that is based on a recently developed dependence measure: ball covariance. Compared with other popular sufficient dimension reduction methods, our approach requires rather mild conditions on the predictors and the response, and is robust to outliers or heavy-tailed distributions. BCov-SDR does not require the specification of a forward regression model and allows for discrete or categorical predictors and multivariate response. The consistency of the BCov-SDR estimator of the central subspace is obtained without imposing any moment conditions on the predictors. Simulations and real data studies illustrate the applicability and versatility of our proposed method.<\/p>\r\n\r\n<div data-wp-interactive=\"core\/file\" class=\"wp-block-file\"><object data-wp-bind--hidden=\"!state.hasPdfPreview\" hidden class=\"wp-block-file__embed\" style=\"width: 100%; height: 600px;\" data=\"https:\/\/changjinyuan.com\/wp-content\/uploads\/2024\/11\/\u5f20\u4f73-Chen-X.-2019.-Robust-sufficient-dimension-reduction-via-ball-covariance-Computational-Statistics-Data-Analysis-Vol.-140-pp.-144-154.pdf\" type=\"application\/pdf\" width=\"300\" height=\"150\" aria-label=\"\u5d4c\u5165 \u5f20\u4f73 &amp; Chen, X. (2019). Robust sufficient dimension reduction via ball covariance, Computational Statistics &amp; Data Analysis, Vol. 140, pp. 144-154.\"><\/object><a id=\"wp-block-file--media-d26cd643-3af2-4642-a9fe-3564f038b1a7\" href=\"https:\/\/changjinyuan.com\/wp-content\/uploads\/2024\/11\/\u5f20\u4f73-Chen-X.-2019.-Robust-sufficient-dimension-reduction-via-ball-covariance-Computational-Statistics-Data-Analysis-Vol.-140-pp.-144-154.pdf\">\u5f20\u4f73 &amp; Chen, X. (2019). Robust sufficient dimension reduction via ball covariance, Computational Statistics &amp; Data Analysis, Vol. 140, pp. 144-154.<\/a><a class=\"wp-block-file__button wp-element-button\" href=\"https:\/\/changjinyuan.com\/wp-content\/uploads\/2024\/11\/\u5f20\u4f73-Chen-X.-2019.-Robust-sufficient-dimension-reduction-via-ball-covariance-Computational-Statistics-Data-Analysis-Vol.-140-pp.-144-154.pdf\" download=\"\" aria-describedby=\"wp-block-file--media-d26cd643-3af2-4642-a9fe-3564f038b1a7\">\u4e0b\u8f7d<\/a><\/div>\r\n","protected":false},"excerpt":{"rendered":"<p>Sufficient dimension reduction is an important branch of dimension reduction, which includes variable selection and projection methods. Most of the sufficient dimension reduction methods are sensitive to outliers and heavy-tailed predictors, and require strict restrictions on the predictors and the response. In order to widen the applicability of sufficient dimension reduction, we propose BCov-SDR, a novel sufficient dimension reduction approach that is based on a recently developed dependence measure: ball covariance. Compared with other popular sufficient dimension reduction methods, our approach requires rather mild conditions on the predictors and the response, and is robust to outliers or heavy-tailed distributions. BCov-SDR does not require the specification of a forward regression model and allows for discrete or categorical predictors and multivariate response. The consistency of the BCov-SDR estimator of the central subspace is obtained without imposing any moment conditions on the predictors. Simulations and real data studies illustrate the applicability and versatility of our proposed method.<\/p>\n","protected":false},"author":1,"featured_media":2884,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[15],"tags":[],"class_list":["post-2565","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publications-all"],"acf":[],"lang":"cn","translations":{"cn":2565,"en":3096},"pll_sync_post":[],"_links":{"self":[{"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts\/2565","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/comments?post=2565"}],"version-history":[{"count":5,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts\/2565\/revisions"}],"predecessor-version":[{"id":3778,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts\/2565\/revisions\/3778"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/media\/2884"}],"wp:attachment":[{"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/media?parent=2565"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/categories?post=2565"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/tags?post=2565"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}