{"id":683,"date":"2021-09-13T19:23:37","date_gmt":"2021-09-13T22:23:37","guid":{"rendered":"https:\/\/www.ufrgs.br\/ppgest\/?page_id=683"},"modified":"2021-11-26T20:02:07","modified_gmt":"2021-11-26T23:02:07","slug":"seminarios2021","status":"publish","type":"page","link":"https:\/\/www.ufrgs.br\/ppgest\/seminarios2021\/","title":{"rendered":"2021"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"683\" class=\"elementor elementor-683\">\n\t\t\t\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-5b693be8 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5b693be8\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-132d17e1\" data-id=\"132d17e1\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-67fb917e elementor-widget elementor-widget-text-editor\" data-id=\"67fb917e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.6.2 - 04-04-2022 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#818a91;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#818a91;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<h3>Semin\u00e1rios indicados com * est\u00e3o dispon\u00edveis no YouTube<\/h3>\n<p>&nbsp;<\/p>\n<h3><span style=\"text-decoration: underline;\"><span style=\"color: #000000;\"> <br \/><strong>2\u00ba Trimestre:<\/strong><\/span><\/span><\/h3>\n<p>&nbsp;<\/p>\n<p><span style=\"color: var( --e-global-color-text ); font-size: 1.6rem;\"><span class=\"collapseomatic \" id=\"id6ae492a4cde56\"  tabindex=\"0\" title=\"Semin\u00e1rio 12\/07\/2021. Prof F\u00e1bio Bayer (UFSM\/PPGEst-UFRGS)*\"    >Semin\u00e1rio 12\/07\/2021. Prof F\u00e1bio Bayer (UFSM\/PPGEst-UFRGS)*<\/span><div id=\"target-id6ae492a4cde56\" class=\"collapseomatic_content \"><\/span><\/p>\n<p><strong>T\u00edtulo:<\/strong> 2-D Rayleigh Autoregressive Moving Average Model for SAR Image Modeling<\/p>\n<p>Two-dimensional (2-D) autoregressive moving average (ARMA) models are commonly applied to describe real-world image data, usually assuming Gaussian or symmetric noise. However, real-world data often present non-Gaussian signals, with asymmetrical distributions and strictly positive values. In particular, SAR images are known to be well characterized by the Rayleigh distribution. In this context, this paper introduces an ARMA model tailored for 2-D Rayleigh-distributed data &#8212; the 2-D RARMA model. The 2-D RARMA model is derived and conditional likelihood inferences are discussed. The proposed model was submitted to extensive Monte Carlo simulations to evaluate the performance of the conditional maximum likelihood estimators. Moreover, in the context of SAR image processing, two comprehensive numerical experiments were performed comparing anomaly detection and image modeling results of the proposed model with traditional 2-D ARMA models and competing methods in the literature.<br \/>Obs. Artigo coautorado com Bruna Gregory Palm e Renato J. Cintra.<\/p>\n<p><div class=\"ast-oembed-container\" style=\"height: 100%;\"><iframe loading=\"lazy\" title=\"Seminario (12\/07\/21)\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/UQZUFhS8oSo?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/div><\/p>\n<p><\/div><\/p>\n<p><span class=\"collapseomatic \" id=\"id6ae492a4cde87\"  tabindex=\"0\" title=\"Semin\u00e1rio 19\/07\/2021. Prof Marcos Oliveira Prates (UFMG)*\"    >Semin\u00e1rio 19\/07\/2021. Prof Marcos Oliveira Prates (UFMG)*<\/span><div id=\"target-id6ae492a4cde87\" class=\"collapseomatic_content \"><\/p>\n<p><strong>T\u00edtulo:<\/strong> Spatial Confounding Beyond Generalized Linear Mixed Models: Extension to Shared Components and Spatial Frailty Models<\/p>\n<p>Spatial confounding is defined as the confounding between the fixed and spatial random effects in generalized linear mixed models (GLMMs). It gained attention in the past years, as it may generate unexpected results in modeling. We introduce solutions to alleviate the spatial confounding beyond GLMMs for two families of statistical models. In the shared component models, multiple count responses are recorded at each spatial location, which may exhibit similar spatial patterns. Therefore, the spatial effect terms may be shared between the outcomes in addition to specifics spatial patterns. Our proposal relies on the use of modified spatial structures for each shared component and specific effects. Spatial frailty models can incorporate spatially structured effects and it is common to observe more than one sample unit per area which means that the support of fixed and spatial effects differs. Thus, we introduce a projection-based approach for reducing the dimension of the data. An R package named &#8220;RASCO: An R package to Alleviate Spatial Confounding&#8221; is provided. Cases of lung and bronchus cancer in the state of California are investigated under both methodologies and the results prove the efficiency of the proposed methodology.<\/p>\n<p><div class=\"ast-oembed-container\" style=\"height: 100%;\"><iframe loading=\"lazy\" title=\"Seminario (19\/07\/21)\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/sxvhD7CKlyo?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/div><\/p>\n<p><\/div><\/p>\n<p><!-- \/wp:shortcode -->\n\n<!-- wp:shortcode --><span class=\"collapseomatic \" id=\"id6ae492a4cde99\"  tabindex=\"0\" title=\"Semin\u00e1rio 26\/07\/2021. Profa. Silvana Schneider (PPGEst\/UFRGS)*\"    >Semin\u00e1rio 26\/07\/2021. Profa. Silvana Schneider (PPGEst\/UFRGS)*<\/span><div id=\"target-id6ae492a4cde99\" class=\"collapseomatic_content \"><\/p>\n<p><!-- wp:paragraph --><\/p>\n<p><!-- \/wp:paragraph --><\/p>\n<p><!-- wp:group --><\/p>\n<p><strong>T\u00edtulo:<\/strong> Abordagens para analisar dados de sobreviv\u00eancia com censura dependente<\/p>\n<p>Normalmente, assume-se que os mecanismos geradores de censura n\u00e3o s\u00e3o relacionados com o evento de interesse. Por\u00e9m, essa suposi\u00e7\u00e3o pode ser inadequada em muitos contextos, resultando em infer\u00eancias enganosas. Com o objetivo de acomodar a depend\u00eancia entre os tempos de falha e censura, propomos abordagens embasadas nos modelos de fragilidade. Para tal, apresentamos uma vers\u00e3o completamente param\u00e9trica, utilizando a distribui\u00e7\u00e3o Weibull e uma vers\u00e3o semiparam\u00e9trica, considerando a distribui\u00e7\u00e3o Exponencial por Partes. Realizamos estudos de simula\u00e7\u00e3o Monte Carlo considerando tr\u00eas cen\u00e1rios: dados gerados com correla\u00e7\u00f5es positiva, negativa e nula entre os tempos de falha e censura. Por \u00faltimo, apresentamos uma aplica\u00e7\u00e3o em dados sobre mortalidade em centros de di\u00e1lise renal dos Estados Unidos, cedidos pelo estudo Dialysis Outcomes and Practice Patterns Study (DOPPS).<\/p>\n<p><div class=\"ast-oembed-container\" style=\"height: 100%;\"><iframe loading=\"lazy\" title=\"Semin\u00e1rio (26\/07\/21)\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/B76rKmcroeo?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/div><\/p>\n<p><\/div><!-- \/wp:shortcode --><\/p>\n<p><!-- wp:shortcode --><span class=\"collapseomatic \" id=\"id6ae492a4cdea7\"  tabindex=\"0\" title=\"Semin\u00e1rio 02\/08\/2021. Profa. Renata Rojas Guerra (UFSM)*\"    >Semin\u00e1rio 02\/08\/2021. Profa. Renata Rojas Guerra (UFSM)*<\/span><div id=\"target-id6ae492a4cdea7\" class=\"collapseomatic_content \"><\/p>\n<p><strong>T\u00edtulo<\/strong>: Fam\u00edlias Weibull estendidas unit\u00e1rias: novas alternativas para a resolu\u00e7\u00e3o de problemas industriais e an\u00e1lise de indicadores educacionais<\/p>\n<p>Este semin\u00e1rio tem por objetivo apresentar a proposta e o desenvolvimento das fam\u00edlias Weibull estendidas unit\u00e1rias (UEW). As referidas fam\u00edlias s\u00e3o classes de distribui\u00e7\u00f5es de probabilidade com suporte duplamente limitado derivadas a partir de transforma\u00e7\u00f5es em vari\u00e1veis aleat\u00f3rias com distribui\u00e7\u00e3o Weibull estendida. Ser\u00e3o abordadas tr\u00eas diferentes fam\u00edlias UEW, destacando alguns casos particulares que t\u00eam sido explorados para o monitoramento de processos produtivos e de indicadores sociais definidos em intervalos duplamente limitados. A potencialidade das fam\u00edlias UEW \u00e9 ilustrada atrav\u00e9s de seu ajuste para as taxas de evas\u00e3o em cursos de gradua\u00e7\u00e3o ofertados por Institui\u00e7\u00f5es de Ensino Superior brasileiras. Al\u00e9m disso, ser\u00e3o apresentados alguns avan\u00e7os no contexto de estima\u00e7\u00e3o da fun\u00e7\u00e3o de confiabilidade em sistemas de tens\u00e3o-resist\u00eancia com m\u00faltiplos componentes.<\/p>\n<p><div class=\"ast-oembed-container\" style=\"height: 100%;\"><iframe loading=\"lazy\" title=\"Semin\u00e1rio (02\/08\/21)\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/K64mgGnQzuw?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/div><\/p>\n<p><\/div><!-- \/wp:shortcode --><\/p>\n<p><span class=\"collapseomatic \" id=\"id6ae492a4cdeb2\"  tabindex=\"0\" title=\"Semin\u00e1rio 09\/08\/2021. Prof. Marcelo Bourguignon (UFRN)*\"    >Semin\u00e1rio 09\/08\/2021. Prof. Marcelo Bourguignon (UFRN)*<\/span><div id=\"target-id6ae492a4cdeb2\" class=\"collapseomatic_content \"><\/p>\n<p><strong>T\u00edtulo<\/strong>: Reparameterized regression models<\/p>\n<p>Regression models are typically constructed to model the mean of a distribution. However, the density of several distributions is not indexed by the mean. In this context, this work provides a collection of regression models considering new parameterizations in terms of the mean and precision parameters. The main advantage of our new parametrizations is the straightforward interpretation of the regression coefficients in terms of the expectation, as usual in the context of generalized linear models.<\/p>\n<p><div class=\"ast-oembed-container\" style=\"height: 100%;\"><iframe loading=\"lazy\" title=\"Seminario (09\/08\/21)\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/QD8yicS__98?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/div><\/p>\n<p><\/div><\/p>\n<p><!-- \/wp:group --><\/p>\n<p><!-- wp:paragraph --><\/p>\n<p><!-- \/wp:paragraph --><\/p>\n<p><!-- wp:group --><\/p>\n<p><!-- wp:shortcode --><span class=\"collapseomatic \" id=\"id6ae492a4cdebd\"  tabindex=\"0\" title=\"Semin\u00e1rio 16\/08\/2021. Profa. Gabriela Bettella Cybis (PPGEst\/UFRGS)*\"    >Semin\u00e1rio 16\/08\/2021. Profa. Gabriela Bettella Cybis (PPGEst\/UFRGS)*<\/span><div id=\"target-id6ae492a4cdebd\" class=\"collapseomatic_content \"><\/p>\n<p><strong>T\u00edtulo<\/strong>: Desvendando a Din\u00e2mica Espa\u00e7o-Temporal do COVID19 com Base nas suas Sequ\u00eancias Gen\u00e9ticas: Abordagens Utilizando Filogenias Bayesianas e Simula\u00e7\u00f5es de Monte Carlo<\/p>\n<p>Sars-Cov-2, o v\u00edrus causador do COVID19 \u00e9 um dos pat\u00f3genos mais intensamente estudados da hist\u00f3ria. Nesse contexto, a disponibilidade de sequ\u00eancias gen\u00e9ticas do v\u00edrus em bancos de dados de livre acesso \u00e9 sem precedentes. Tais sequ\u00eancias s\u00e3o instrumentais na reconstru\u00e7\u00e3o da din\u00e2mica migrat\u00f3ria do v\u00edrus e na detec\u00e7\u00e3o da evolu\u00e7\u00e3o de novas variantes. Nesse semin\u00e1rio, apresentarei duas abordagens para o estudo de tais processos com base nas sequ\u00eancias gen\u00e9ticas. Na primeira, m\u00e9todos da filodin\u00e2mica bayesiana ser\u00e3o empregados para ajudar a identificar introdu\u00e7\u00f5es do v\u00edrus em uma regi\u00e3o e circula\u00e7\u00e3o local. Na segunda, reamostragem e simula\u00e7\u00f5es de Monte Carlo ser\u00e3o empregadas para identificar os efeitos de diferentes estrat\u00e9gias de coletas de dados na identifica\u00e7\u00e3o de variantes do v\u00edrus.<\/p>\n<p><div class=\"ast-oembed-container\" style=\"height: 100%;\"><iframe loading=\"lazy\" title=\"Seminario (16\/08\/21)\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/2sXh0SO_uTg?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/div><\/p>\n<p><\/div><!-- \/wp:shortcode --><\/p>\n<p><!-- \/wp:group --><\/p>\n<p><!-- wp:paragraph --><\/p>\n<p><!-- \/wp:paragraph --><\/p>\n<p><!-- wp:shortcode --><span class=\"collapseomatic \" id=\"id6ae492a4cdec7\"  tabindex=\"0\" title=\"Semin\u00e1rio 23\/08\/2021. Prof. Guilherme Vieira Nunes Ludwig (UNICAMP)*\"    >Semin\u00e1rio 23\/08\/2021. Prof. Guilherme Vieira Nunes Ludwig (UNICAMP)*<\/span><div id=\"target-id6ae492a4cdec7\" class=\"collapseomatic_content \"><\/p>\n<p><strong>T\u00edtulo<\/strong>: Scalable modeling of nonstationary covariance functions with non-folding B-spline deformations<\/p>\n<p>Sars-Cov-2, o v\u00edrus causador do COVID19 \u00e9 um dos pat\u00f3genos mais intensamente estudados da hist\u00f3ria. Nesse contexto, a disponibilidade de sequ\u00eancias gen\u00e9ticas do v\u00edrus em bancos de dados de livre acesso \u00e9 sem precedentes. Tais sequ\u00eancias s\u00e3o instrumentais na reconstru\u00e7\u00e3o da din\u00e2mica migrat\u00f3ria do v\u00edrus e na detec\u00e7\u00e3o da evolu\u00e7\u00e3o de novas variantes. Nesse semin\u00e1rio, apresentarei duas abordagens para o estudo de tais processos com base nas sequ\u00eancias gen\u00e9ticas. Na primeira, m\u00e9todos da filodin\u00e2mica bayesiana ser\u00e3o empregados para ajudar a identificar introdu\u00e7\u00f5es do v\u00edrus em uma regi\u00e3o e circula\u00e7\u00e3o local. Na segunda, reamostragem e simula\u00e7\u00f5es de Monte Carlo ser\u00e3o empregadas para identificar os efeitos de diferentes estrat\u00e9gias de coletas de dados na identifica\u00e7\u00e3o de variantes do v\u00edrus.<\/p>\n<p><div class=\"ast-oembed-container\" style=\"height: 100%;\"><iframe loading=\"lazy\" title=\"Semin\u00e1rio (23\/08\/21)\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/mSLy6uADyRY?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/div><\/p>\n<p><\/div><\/p>\n<p><span class=\"collapseomatic \" id=\"id6ae492a4cdedb\"  tabindex=\"0\" title=\"Semin\u00e1rio 30\/08\/2021. Profa. Cira E.G. Otiniano (UNB)\"    >Semin\u00e1rio 30\/08\/2021. Profa. Cira E.G. Otiniano (UNB)<\/span><div id=\"target-id6ae492a4cdedb\" class=\"collapseomatic_content \"><\/p>\n<p><strong>T\u00edtulo<\/strong>: Modelos alternativos para dados heterog\u00eaneos: distribui\u00e7\u00f5es GEV bimodais<\/p>\n<p>A distribui\u00e7\u00e3o generalizada de valores extremos (GEV) \u00e9 um modelo estat\u00edstico muito popular devido \u00e0 sua ampla aplicabilidade por exemplo em estudos hidrol\u00f3gicos, ambientais, financeiros ou de confiabilidade. Neste trabalho, introduzimos um modelo GEV bimodal que pode ser uma alternativa para modelar dados heterog\u00eaneos de diversas \u00e1reas. Derivamos as formas anal\u00edticas de sua fun\u00e7\u00e3o de densidade de probabilidade, fun\u00e7\u00e3o de distribui\u00e7\u00e3o cumulativa e fun\u00e7\u00e3o de taxa de risco. Al\u00e9m disso, discutimos as propriedades desta densidade como modo, bimodalidade, fun\u00e7\u00e3o geradora de momento e momentos. Nossos resultados foram verificados usando o m\u00e9todo de simula\u00e7\u00e3o de Monte Carlo por cadeia de Markov. O m\u00e9todo de m\u00e1xima verossimilhan\u00e7a \u00e9 usado para estimativa de par\u00e2metros. Finalmente, tamb\u00e9m realizamos uma aplica\u00e7\u00e3o de nossos modelos para dados reais.<\/p>\n<p><\/div><\/p>\n<p><span class=\"collapseomatic \" id=\"id6ae492a4cdee6\"  tabindex=\"0\" title=\"Semin\u00e1rio 06\/09\/2021. Prof. Francisco Cysneiros (UFPE)\"    >Semin\u00e1rio 06\/09\/2021. Prof. Francisco Cysneiros (UFPE)<\/span><div id=\"target-id6ae492a4cdee6\" class=\"collapseomatic_content \"><\/p>\n<p><strong>T\u00edtulo<\/strong>: Modelos de Regress\u00e3o El\u00edpticos Geograficamente Ponderados<\/p>\n<p>O uso dos modelos de regress\u00e3o cl\u00e1ssicos no contexto de dados espaciais podem levar a interpreta\u00e7\u00f5es err\u00f4neas, pois este n\u00e3o leva em considera\u00e7\u00e3o as informa\u00e7\u00f5es geoespaciais. Neste caso, o desenvolvimento de modelos geograficamente ponderados para o estudo da n\u00e3o estacionariedade espacial dos par\u00e2metros de regress\u00e3o \u00e9 uma alternativa bastante atrativa. No entanto, esses modelos costumam ser sens\u00edveis na presen\u00e7a de valores discrepantes. Neste trabalho, desenvolvemos extens\u00f5es da regress\u00e3o geograficamente ponderada utilizando a fam\u00edlia de distribui\u00e7\u00e3o el\u00edptica para a suposi\u00e7\u00e3o dos erros aleat\u00f3rios. O uso desta fam\u00edlia de distribui\u00e7\u00e3o permite ajustar um modelo menos sens\u00edvel na presen\u00e7a de observa\u00e7\u00f5es discrepantes, uma vez que abrange distribui\u00e7\u00f5es com caudas mais pesadas do que a normal. M\u00e9todo de estima\u00e7\u00e3o\u00a0 e\u00a0 estudos de simula\u00e7\u00e3o sob diferentes cen\u00e1rios s\u00e3o apresentados. An\u00e1lise de res\u00edduos e medidas diagn\u00f3stico com base na abordagem de influ\u00eancia global e local s\u00e3o desenvolvidos. Uma\u00a0 aplica\u00e7\u00e3o referente ao estudo do desenvolvimento humano e da criminalidade nos munic\u00edpios do estado do Cear\u00e1, Brasil \u00e9 discutido.<\/p>\n<p><\/div><\/p>\n<p><span class=\"collapseomatic \" id=\"id6ae492a4cdef3\"  tabindex=\"0\" title=\"Semin\u00e1rio 27\/09\/2021. Prof. Eduardo de Oliveira Horta (UFRGS)*\"    >Semin\u00e1rio 27\/09\/2021. Prof. Eduardo de Oliveira Horta (UFRGS)*<\/span><div id=\"target-id6ae492a4cdef3\" class=\"collapseomatic_content \"><\/p>\n<p><strong>T\u00edtulo<\/strong>: <span data-sheets-value=\"{\" data-sheets-userformat=\"{\">Conditional mode: A smoothed quantile regression approach<\/span><\/p>\n<p>We propose to smooth the objective function, rather than only the indicator on the check function, in a linear <span class=\"il\">quantile<\/span>\u00a0regression context. Not only does the resulting smoothed\u00a0<span class=\"il\">quantile<\/span>\u00a0regression estimator yield a lower mean squared error and a more accurate Bahadur\u2013Kiefer representation than the standard estimator, but it is also asymptotically differentiable. We exploit the latter to propose a\u00a0<span class=\"il\">quantile<\/span>\u00a0density estimator that does not suffer from the curse of dimensionality. This means estimating the conditional density function without worrying about the dimension of the covariate vector. It also allows for two-stage efficient\u00a0<span class=\"il\">quantile<\/span>\u00a0regression estimation. Our asymptotic theory holds uniformly with respect to the bandwidth and\u00a0<span class=\"il\">quantile<\/span>\u00a0level. Finally, we propose a rule of thumb for choosing the smoothing bandwidth that should approximate well the optimal bandwidth. Simulations confirm that our smoothed\u00a0<span class=\"il\">quantile<\/span>\u00a0regression estimator indeed performs very well in finite samples.<\/p>\n<p><div class=\"ast-oembed-container\" style=\"height: 100%;\"><iframe loading=\"lazy\" title=\"Seminario  (27\/09\/21)\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/QLUnPsrZXds?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/div><\/p>\n<p><\/div><\/p>\n<h3><span style=\"text-decoration: underline;\"><span style=\"color: #000000;\"> <br \/><strong>3\u00ba Trimestre:<\/strong><\/span><\/span><\/h3>\n<p>&nbsp;<\/p>\n<p><span class=\"collapseomatic \" id=\"id6ae492a4cdefd\"  tabindex=\"0\" title=\"Semin\u00e1rio 18\/10\/2021. Prof. Rodrigo Lambert (UFU)*\"    >Semin\u00e1rio 18\/10\/2021. Prof. Rodrigo Lambert (UFU)*<\/span><div id=\"target-id6ae492a4cdefd\" class=\"collapseomatic_content \"><\/p>\n<p><strong>T\u00edtulo:\u00a0<\/strong>Tempo de primeiro retorno, e fun\u00e7\u00e3o de sobreposi\u00e7\u00e3o<\/p>\n<p> O estudo de tempos de primeiro retorno e fun\u00e7\u00e3o de sobreposi\u00e7\u00e3o em sequ\u00eancias simb\u00f3licas tem forte apelo em aplica\u00e7\u00f5es. Desde gen\u00e9tica com o estudo de sequ\u00eancias de DNA at\u00e9 a teoria da informa\u00e7\u00e3o com o estudo de algoritmos de compress\u00e3o, tal assunto mostra uma ferramenta potencialmente \u00fatil para atacar problemas que pertencem a diferentes \u00e1reas do conhecimento. Nessa apresenta\u00e7\u00e3o, come\u00e7arei motivando e dando as defini\u00e7\u00f5es de tempo de primeiro retorno e fun\u00e7\u00e3o de sobreposi\u00e7\u00e3o, e comentarei alguns resultados conhecidos da literatura. Finalmente, apresentarei um resultado recentemente obtido com E. A. Rada-Mora (UFABC).<\/p>\n<p><div class=\"ast-oembed-container\" style=\"height: 100%;\"><iframe loading=\"lazy\" title=\"Semin\u00e1rio (18\/10\/21)\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/m0AlpzweZhg?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/div><\/p>\n<p><\/div><\/p>\n<p><span class=\"collapseomatic \" id=\"id6ae492a4cdf07\"  tabindex=\"0\" title=\"Semin\u00e1rio 25\/10\/2021. Prof. Hudson da Silva Torrent (UFRGS)*\"    >Semin\u00e1rio 25\/10\/2021. Prof. Hudson da Silva Torrent (UFRGS)*<\/span><div id=\"target-id6ae492a4cdf07\" class=\"collapseomatic_content \"><\/p>\n<p><strong>T\u00edtulo:<\/strong> \u00a0Semiparametric Portfolios: Improving Portfolio Performance by Exploiting Non-Linearities in Firm Characteristics.<\/p>\n<p>We present a semiparametric portfolio optimization method in which portfolio weights are parameterized as a non-linear function of firm characteristics. This approach generalizes the linear parametric portfolio policy of Brandt et al. (2009) and can be applied to high-dimensional problems involving hundreds or thousands of stocks. An empirical implementation exploiting the size, value, and momentum anomalies in the universe of CRSP stocks reveal that non-linearities, as well as interaction effects, are both important and complementary for the portfolio construction problem. Moreover, an out-of-sample evaluation indicates that the semiparametric strategies perform well in terms of returns, risk, and risk-adjusted returns both in the absence and in the presence of transaction costs. Our evidence suggests that allowing for a more flexible relation between portfolio weights and firm characteristics can provide a more accurate description of the empirical patterns seen in data.<\/p><strong><br \/><\/strong><\/p>\n<p><div class=\"ast-oembed-container\" style=\"height: 100%;\"><iframe loading=\"lazy\" title=\"Seminario   (25\/10\/2021)\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/dN0AgjxA9Dw?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/div><\/p>\n<p><\/div><\/p>\n<p><span class=\"collapseomatic \" id=\"id6ae492a4cdf14\"  tabindex=\"0\" title=\"Semin\u00e1rio 05\/11\/2021. Prof. Leonardo Bastos (Fiocruz)\"    >Semin\u00e1rio 05\/11\/2021. Prof. Leonardo Bastos (Fiocruz)<\/span><div id=\"target-id6ae492a4cdf14\" class=\"collapseomatic_content \"><\/p>\n<p><strong>T\u00edtulo<\/strong>: Age-dependent nowcasting deaths and hospitalised cases Covid-19 in Brazil<\/p>\n<p>The coronavirus disease (COVID-19) pandemic continues to cause amassive burden in the world, especially in countries such as Brazil, with poor implementation of strategies to mitigate the transmission of SARS-CoV-2. The number of cases, severe cases, and deaths by COVID-19 are important indicators of how the COVID-19 epidemic is affecting a particular region and can be used by decision-makers to act in order to reduce morbidity and mortality. However, a common problem with surveillance data is reporting delays, whereby cases and deaths are recorded in the surveillance system days or even weeks after they occurred. Statistical models can estimate the actual number of severe cases and deaths due to COVID-19 accounting for the delays (nowcasting) by age groups. We proposed a Bayesian hierarchical model to nowcast deaths and hospitalised cases for Brazil and also for the 27 federal units. Finally, we provide some general discussion about the COVID-19 situation in Brazil.<\/p> \n<p><\/div><\/p>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Semin\u00e1rios indicados com * est\u00e3o dispon\u00edveis no YouTube &nbsp; 2\u00ba Trimestre: &nbsp; 3\u00ba Trimestre: &nbsp;<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","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":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","footnotes":""},"_links":{"self":[{"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/pages\/683"}],"collection":[{"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/types\/page"}],"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=683"}],"version-history":[{"count":43,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/pages\/683\/revisions"}],"predecessor-version":[{"id":827,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/pages\/683\/revisions\/827"}],"wp:attachment":[{"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/media?parent=683"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}