{"id":2284,"date":"2025-06-18T20:10:08","date_gmt":"2025-06-18T23:10:08","guid":{"rendered":"https:\/\/www.ufrgs.br\/ppgest\/?p=2284"},"modified":"2025-06-18T20:10:46","modified_gmt":"2025-06-18T23:10:46","slug":"2284-2","status":"publish","type":"post","link":"https:\/\/www.ufrgs.br\/ppgest\/2284-2\/","title":{"rendered":"Ciclo de Semin\u00e1rios 2025 &#8211; Prof. Dr. Alu\u00edsio Pinheiro"},"content":{"rendered":"<p>Temos o prazer de convidar a todos para mais um semin\u00e1rio do Programa de P\u00f3s-Gradua\u00e7\u00e3o em Estat\u00edstica da UFRGS (PPGEst-UFRGS).<\/p>\n<div id=\"v1forwardbody1\">\n<div id=\"v1v1forwardbody1\">\n<div id=\"v1v1v1forwardbody1\">\n<div id=\"v1v1v1v1forwardbody1\">\n<div id=\"v1v1v1v1v1v1v1v1v1v1v1v1forwardbody1\">\n<div id=\"v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1forwardbody1\">\n<div id=\"v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1forwardbody1\">\n<div id=\"v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1forwardbody1\">\n<div id=\"v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1forwardbody1\">\n<div class=\"v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1xdj266r v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x11i5rnm v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1xat24cr v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x1mh8g0r v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x1vvkbs v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x126k92a\">\n<div dir=\"auto\"><\/div>\n<\/div>\n<div class=\"v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x11i5rnm v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1xat24cr v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x1mh8g0r v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x1vvkbs v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1xtlvy1s v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x126k92a\">\n<div dir=\"auto\">\n<p><strong>T\u00edtulo:<\/strong> Wavelet feature screening in high dimensional regression problems.<\/p>\n<p style=\"text-align: left;\"><strong>Resumo: <\/strong>In modern data analysis, the number p of explanatory variables is commonly much larger than the sample size n. Some classic methods to deal with this type of problem are sub-sampling covariates, ridge regression and Lasso (Friedman et al., 2009). Besides the high computational cost, p much larger than n may also affect the performance of variable selection methods. This motivates screening covariates.<\/p>\n<p>Thence, an initial screening of relevant covariates is common in high-dimensional models. One important issue here is that many important features might have a relevant albeit highly nonlinear relation with the response. We propose a wavelet feature screening method that can be easily implemented, and we prove that, under suitable conditions, it captures the true covariates with high probability. Simulation results also show that our approach outperforms other screening methods in highly nonlinear models. We apply feature screening to two datasets about ozone concentration and epilepsy. In both applications, the proposed method selects features that match findings in the literature of their respective research fields, illustrating the applicability of feature screening.<\/p>\n<p>This is a joint work with Rodney Fonseca and Pedro\u00a0\u00a0Morettin.<\/p>\n<p>&nbsp;<\/p>\n<\/div>\n<\/div>\n<div class=\"v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x11i5rnm v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1xat24cr v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x1mh8g0r v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x1vvkbs v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1xtlvy1s v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x126k92a\">\n<div dir=\"auto\"><strong>Palestrante:<\/strong> Prof. Dr. Alu\u00edsio de Souza Pinheiro \u2013 UNICAMP<br \/>\n<strong>Mediador:\u00a0<\/strong>Prof. Dr. Flavio Augusto Ziegelmann<\/div>\n<\/div>\n<div class=\"v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x11i5rnm v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1xat24cr v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x1mh8g0r v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x1vvkbs v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1xtlvy1s v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x126k92a\">\n<div dir=\"auto\"><strong>Data:<\/strong> 24\/06\/2025<\/div>\n<div dir=\"auto\"><strong>Hor\u00e1rio<\/strong>:13h15<\/div>\n<div dir=\"auto\"><strong>Local: <\/strong>Sala <span class=\"OYPEnA font-feature-liga-off font-feature-clig-off font-feature-calt-off text-decoration-none text-strikethrough-none\">A214, pr\u00e9dio 43111 <\/span>, Anfiteatro da qu\u00edmica &#8211; UFRGS<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Temos o prazer de convidar a todos para mais um semin\u00e1rio do Programa de P\u00f3s-Gradua\u00e7\u00e3o em Estat\u00edstica da UFRGS (PPGEst-UFRGS). T\u00edtulo: Wavelet feature screening in high dimensional regression problems. Resumo: In modern data analysis, the number p of explanatory variables is commonly much larger than the sample size n. Some classic methods to deal with &hellip;<\/p>\n<p class=\"read-more\"> <a class=\"\" href=\"https:\/\/www.ufrgs.br\/ppgest\/2284-2\/\"> <span class=\"screen-reader-text\">Ciclo de Semin\u00e1rios 2025 &#8211; Prof. Dr. Alu\u00edsio Pinheiro<\/span> Read More &raquo;<\/a><\/p>\n","protected":false},"author":1,"featured_media":2286,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"default","ast-global-header-display":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","footnotes":""},"categories":[13],"tags":[],"_links":{"self":[{"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/posts\/2284"}],"collection":[{"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/comments?post=2284"}],"version-history":[{"count":2,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/posts\/2284\/revisions"}],"predecessor-version":[{"id":2288,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/posts\/2284\/revisions\/2288"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/media\/2286"}],"wp:attachment":[{"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/media?parent=2284"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/categories?post=2284"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/tags?post=2284"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}