{"id":2032,"date":"2024-12-04T16:05:42","date_gmt":"2024-12-04T19:05:42","guid":{"rendered":"https:\/\/www.ufrgs.br\/ppgest\/?p=2032"},"modified":"2024-12-04T16:05:42","modified_gmt":"2024-12-04T19:05:42","slug":"ciclo-de-seminarios-ppgest-2024-3","status":"publish","type":"post","link":"https:\/\/www.ufrgs.br\/ppgest\/ciclo-de-seminarios-ppgest-2024-3\/","title":{"rendered":"Ciclo de Semin\u00e1rios PPGEst 2024"},"content":{"rendered":"

\"\"<\/p>\n

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

\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
<\/div>\n
Informa\u00e7\u00f5es abaixo:<\/div>\n
<\/div>\n<\/div>\n
\n
\n
\n
\n
T\u00edtulo: Local Prediction Sets With Applications to Statistical Inference<\/p>\n

Abstract: \u00a0Quantifying uncertainty is essential for reliable predictive modeling and statistical inference. Traditional conformal methods ensure marginally valid prediction intervals but often lack adaptability and fail to achieve conditional coverage. We introduce a model-agnostic approach leveraging regression trees and Random Forests to construct data-adaptive partitions that approximate local coverage guarantees, enhancing scalability and accuracy of existing approaches. Extending this idea to statistical inference, we propose novel techniques for calibrating confidence sets with finite-sample and asymptotic conditional coverage. These methods rely on simulated data for calibration, making them effective even in small-sample regimes or when nuisance parameters are present. Experiments show these techniques outperform existing approaches, offering practical tools for uncertainty quantification in complex settings.<\/p><\/div>\n<\/div>\n<\/div>\n

\n
<\/div>\n<\/div>\n<\/div>\n
<\/div>\n<\/div>\n
\n
Palestrante<\/strong>: Prof. Doutor Rafael Izbicki\u00a0Department of Statistics\u00a0Federal University of S\u00e3o Carlos (UFSCar)<\/div>\n
<\/div>\n<\/div>\n
\n
Data:<\/strong> 10\/12\/2024<\/div>\n
<\/div>\n
Hor\u00e1rio<\/strong>: 13h30min<\/div>\n
<\/div>\n
Local: P<\/strong>lataforma mconf, no link:https:\/\/mconf.ufrgs.br\/webconf\/ppgest<\/a><\/strong><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/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":"

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). Informa\u00e7\u00f5es abaixo: T\u00edtulo: Local Prediction Sets With Applications to Statistical Inference Abstract: \u00a0Quantifying uncertainty is essential for reliable predictive modeling and statistical inference. Traditional conformal methods ensure marginally valid prediction intervals but often lack adaptability …<\/p>\n

Ciclo de Semin\u00e1rios PPGEst 2024<\/span> Read More »<\/a><\/p>\n","protected":false},"author":1,"featured_media":2033,"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\/2032"}],"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=2032"}],"version-history":[{"count":1,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/posts\/2032\/revisions"}],"predecessor-version":[{"id":2034,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/posts\/2032\/revisions\/2034"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/media\/2033"}],"wp:attachment":[{"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/media?parent=2032"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/categories?post=2032"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/tags?post=2032"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}