{"id":1932,"date":"2024-10-09T07:57:22","date_gmt":"2024-10-09T10:57:22","guid":{"rendered":"https:\/\/www.ufrgs.br\/ppgest\/?p=1932"},"modified":"2024-10-15T08:51:12","modified_gmt":"2024-10-15T11:51:12","slug":"ciclo-de-seminarios-doscentes","status":"publish","type":"post","link":"https:\/\/www.ufrgs.br\/ppgest\/ciclo-de-seminarios-doscentes\/","title":{"rendered":"Ciclo de Semin\u00e1rios Docentes"},"content":{"rendered":"<p><img loading=\"lazy\" decoding=\"async\" class=\"size-medium wp-image-1935 aligncenter\" src=\"https:\/\/www.ufrgs.br\/ppgest\/wp-content\/uploads\/2024\/10\/Ciclo-Docentes-2-300x300.png\" alt=\"\" width=\"300\" height=\"300\" srcset=\"https:\/\/www.ufrgs.br\/ppgest\/wp-content\/uploads\/2024\/10\/Ciclo-Docentes-2-300x300.png 300w, https:\/\/www.ufrgs.br\/ppgest\/wp-content\/uploads\/2024\/10\/Ciclo-Docentes-2-1024x1024.png 1024w, https:\/\/www.ufrgs.br\/ppgest\/wp-content\/uploads\/2024\/10\/Ciclo-Docentes-2-150x150.png 150w, https:\/\/www.ufrgs.br\/ppgest\/wp-content\/uploads\/2024\/10\/Ciclo-Docentes-2-768x768.png 768w, https:\/\/www.ufrgs.br\/ppgest\/wp-content\/uploads\/2024\/10\/Ciclo-Docentes-2-12x12.png 12w, https:\/\/www.ufrgs.br\/ppgest\/wp-content\/uploads\/2024\/10\/Ciclo-Docentes-2.png 1080w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/p>\n<div class=\"v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x11i5rnm v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1xat24cr v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x1mh8g0r v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x1vvkbs v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1xtlvy1s v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x126k92a\">\n<div dir=\"auto\" style=\"text-align: justify;\">T\u00edtulo:\u00a0\u00a0Estimation under shape constraints with log barrier penalty<\/div>\n<div dir=\"auto\" style=\"text-align: justify;\"><\/div>\n<div dir=\"auto\" style=\"text-align: justify;\">Resumo:\u00a0\u00a0This is work in progress with Nianqing Liu (Xiamen), Mohamed Merabtine (QMUL) and Quang Vuong (NYU).A common belief is that imposing inequality constraints only improves estimation when the true parameter lies near the boundary. Indeed, when the true parameter is in the interior of the constraints, the constrained and unconstrained estimators are equivalent at any order. A popular method for estimation under shape constraint employs log barrier penalty, which maximises an objective function penalised with a log barrier constraining estimation. Classical numerical approaches reestimate the parameters decreasing the weight parameter until a convergence criterion is reached. We adopt a statistical interpretation of the penalty weight , which shrinks estimation towards the constraints. Under certain circumstances, the penalty weight can be chosen optimally minimising a Mean Squared Error criterion when the true parameter lies inside the constraints. The statistically optimised constrained estimator improves on the unconstrained and constrained estimators with a magnitude which is proportional to the distance to the constraints and a vanishing power of the sample size.<\/div>\n<div dir=\"auto\" style=\"text-align: justify;\"><\/div>\n<div dir=\"auto\" style=\"text-align: justify;\"><\/div>\n<\/div>\n<div class=\"v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x11i5rnm v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1xat24cr v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x1mh8g0r v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x1vvkbs v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1xtlvy1s v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x126k92a\" style=\"text-align: justify;\">\n<div dir=\"auto\"><strong>Palestrante<\/strong>:\u00a0 Prof. Dr. Emmanuel Guerre (Queen Mary University of London &#8211; UK)<\/div>\n<div dir=\"auto\"><\/div>\n<\/div>\n<div class=\"v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x11i5rnm v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1xat24cr v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x1mh8g0r v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x1vvkbs v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1xtlvy1s v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1v1x126k92a\">\n<div dir=\"auto\" style=\"text-align: justify;\"><strong>Data:<\/strong>\u00a015\/10\/2024<\/div>\n<div dir=\"auto\" style=\"text-align: justify;\"><\/div>\n<div dir=\"auto\" style=\"text-align: justify;\"><strong>Hor\u00e1rio<\/strong>: 13h30min<\/div>\n<div dir=\"auto\" style=\"text-align: justify;\"><\/div>\n<div dir=\"auto\" style=\"text-align: justify;\"><strong>Local:<\/strong>\u00a0Sala A101 do Instituto de Matem\u00e1tica e Estat\u00edstica da UFRGS (Av. Bento Gon\u00e7alves, 9500 Pr\u00e9dio 43-111)<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>T\u00edtulo:\u00a0\u00a0Estimation under shape constraints with log barrier penalty Resumo:\u00a0\u00a0This is work in progress with Nianqing Liu (Xiamen), Mohamed Merabtine (QMUL) and Quang Vuong (NYU).A common belief is that imposing inequality constraints only improves estimation when the true parameter lies near the boundary. Indeed, when the true parameter is in the interior of the constraints, the &hellip;<\/p>\n<p class=\"read-more\"> <a class=\"\" href=\"https:\/\/www.ufrgs.br\/ppgest\/ciclo-de-seminarios-doscentes\/\"> <span class=\"screen-reader-text\">Ciclo de Semin\u00e1rios Docentes<\/span> Read More &raquo;<\/a><\/p>\n","protected":false},"author":1,"featured_media":1935,"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\/1932"}],"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=1932"}],"version-history":[{"count":5,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/posts\/1932\/revisions"}],"predecessor-version":[{"id":1945,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/posts\/1932\/revisions\/1945"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/media\/1935"}],"wp:attachment":[{"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/media?parent=1932"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/categories?post=1932"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/tags?post=1932"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}