{"id":1946,"date":"2024-10-16T13:06:06","date_gmt":"2024-10-16T16:06:06","guid":{"rendered":"https:\/\/www.ufrgs.br\/ppgest\/?p=1946"},"modified":"2024-10-16T13:06:12","modified_gmt":"2024-10-16T16:06:12","slug":"ciclo-de-seminarios-docentes","status":"publish","type":"post","link":"https:\/\/www.ufrgs.br\/ppgest\/ciclo-de-seminarios-docentes\/","title":{"rendered":"Ciclo de Semin\u00e1rios Docentes"},"content":{"rendered":"

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\nTemos 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).<\/span><\/p>\n

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Informa\u00e7\u00f5es abaixo:<\/div>\n
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T\u00edtulo:\u00a0RENeGe(Randomly Edge-Weighted Neighborhood Graphs): A new approach for spatial data<\/div>\n
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Resumo:\u00a0Traditional models for areal data assume a hierarchical structure where one of the components is the random effects that spatially correlate the areas. The conditional autoregressive (CAR) model is the most popular distribution to jointly model the prior uncertainty about these spatial random effects. One limitation of the CAR distribution is the inability of producing high correlations between neighboring areas. We propose a new model for areal data that alleviates this problem. We represent the map by an undirected graph where the nodes are the areas and randomly-weighted edges connect nodes that are neighbors. The model is based on a multivariate Student-$t$ distribution, spatially structured, in which the precision matrix is indirectly built assuming a multivariate distribution for the random edges effects. The edges effects’ joint distribution is a spatial multivariate Student-$t$ that induces another $t$ distribution for the areas’ spatial effects which inherit its capacity to accommodate outliers and heavy-tail behavior. Most important, it can produce a higher marginal correlation between the spatial effects than the CAR model overcoming one of the main limitations to this model. We fit the proposed model to analyze real cancer maps and compared its performance with several state-of-art competitors. Our proposed model provides better fitting in almost all cases. Joint work with Danna L. Cruz and Renato M. Assun\u00e7\u00e3o. This work was supported by CAPES, CNPq and FAPEMIG.<\/div>\n
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Palestrante<\/strong>:\u00a0 Prof\u00aa. Dr\u00aa.\u00a0Rosangela Helena Loschi (UFMG)<\/div>\n
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Data:<\/strong>\u00a022\/10\/2024<\/div>\n
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Hor\u00e1rio<\/strong>: 13h30min<\/div>\n
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Local: P<\/strong>lataforma mconf, no link:https:\/\/mconf.ufrgs.br\/webconf\/ppgest<\/a><\/strong><\/div>\n
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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:\u00a0RENeGe(Randomly Edge-Weighted Neighborhood Graphs): A new approach for spatial data Resumo:\u00a0Traditional models for areal data assume a hierarchical structure where one of the components is the random effects that spatially correlate the areas. …<\/p>\n

Ciclo de Semin\u00e1rios Docentes<\/span> Read More »<\/a><\/p>\n","protected":false},"author":1,"featured_media":1949,"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\/1946"}],"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=1946"}],"version-history":[{"count":2,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/posts\/1946\/revisions"}],"predecessor-version":[{"id":1950,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/posts\/1946\/revisions\/1950"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/media\/1949"}],"wp:attachment":[{"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/media?parent=1946"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/categories?post=1946"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/tags?post=1946"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}