{"id":681,"date":"2021-09-13T18:41:59","date_gmt":"2021-09-13T21:41:59","guid":{"rendered":"https:\/\/www.ufrgs.br\/ppgest\/?page_id=681"},"modified":"2021-09-13T20:00:04","modified_gmt":"2021-09-13T23:00:04","slug":"2020-2","status":"publish","type":"page","link":"https:\/\/www.ufrgs.br\/ppgest\/2020-2\/","title":{"rendered":"2020"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"681\" class=\"elementor elementor-681\">\n\t\t\t\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2be17f59 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2be17f59\" 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-165086b8\" data-id=\"165086b8\" 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-22e3d900 elementor-widget elementor-widget-text-editor\" data-id=\"22e3d900\" 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<p><!-- wp:shortcode --><\/p>\n<p><strong>Semin\u00e1rios indicados com * cont\u00e9m suas grava\u00e7\u00f5es disponibilizadas<\/strong><\/p>\n<p>\u00a0<\/p>\n<p><span class=\"collapseomatic \" id=\"id6ae263c7d8667\"  tabindex=\"0\" title=\"Semin\u00e1rio 21\/09\/2020.  Prof F\u00e1bio Mariano Bayer - UFSM*\"    >Semin\u00e1rio 21\/09\/2020.  Prof F\u00e1bio Mariano Bayer &#8211; UFSM*<\/span><div id=\"target-id6ae263c7d8667\" class=\"collapseomatic_content \"><\/p>\n<p style=\"text-align: left;\"><strong>Title:<\/strong> A Three-dimensional Spatiotemporal Model for Remote Sensing Data Cubes<\/p>\n<p><div class=\"ast-oembed-container\" style=\"height: 100%;\"><iframe loading=\"lazy\" title=\"Semin\u00e1rio (21\/09\/2020)\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/aW9I_8Vfwe0?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 style=\"text-align: left;\"><strong>Resumo:<\/strong> Satellite images from the same scene observed over time can be composed in an image stack, which could be modeled as a three- dimensional (3D) cube. To handle this type of remote sensing data, on the one side unidimensional dynamical models have been considered, modeling each pixel separately along the time (pixel-based approach), exploring the temporal correlation. On the other side, two-dimensional approaches have been considered to process each image at one date, exploring the spatial correlation. In this paper, we propose a new 3D autoregressive (3D-AR) model useful for multitemporal image interpretation exploring the correlation in three dimensions altogether. The 3D-AR model is statistically defined and a robust parameter estimation method is discussed. Tools for filtering, forecasting, and detecting anomalies are also introduced. A Monte Carlo simulation study is performed to evaluate the finite signal length performance of the robust estimation and its sensitivity to outliers. The proposed model is applied to a multitemporal normalized difference vegetation index (NDVI) image stack for filtering, prediction, and anomaly detection purposes. The numerical results show the importance of the proposed 3D-AR model for spatiotemporal remote sensing data interpretation.<\/p>\n<p><\/div><\/p>\n<p><span class=\"collapseomatic \" id=\"id6ae263c7d869c\"  tabindex=\"0\" title=\"Semin\u00e1rio 14\/09\/2020.  Prof Jos\u00e9 Luiz Padilha da Silva - UFPR*\"    >Semin\u00e1rio 14\/09\/2020.  Prof Jos\u00e9 Luiz Padilha da Silva &#8211; UFPR*<\/span><div id=\"target-id6ae263c7d869c\" class=\"collapseomatic_content \"><\/p>\n<p><strong>T\u00edtulo:<\/strong> Modelos Marginais para Respostas Bin\u00e1rias com Estruturas Hier\u00e1rquicas de Agrupamento<\/p>\n<p><div class=\"ast-oembed-container\" style=\"height: 100%;\"><iframe loading=\"lazy\" title=\"Semin\u00e1rio (14\/09\/2020)\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/0g5O2BlhtKY?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><strong>Resumo<\/strong>: Estudos que envolvem respostas bin\u00e1rias com estruturas hier\u00e1rquicas de agrupamento s\u00e3o frequentemente encontrados nas diversas \u00e1reas do conhecimento. H\u00e1 situa\u00e7\u00f5es em que o interesse reside primariamente em modelar a probabilidade marginal da resposta. Neste caso, modelos marginais de primeira ordem, ajustados pelo m\u00e9todo GEE1 (Generalized Estimating Equations), oferecem uma solu\u00e7\u00e3o elegante por sua facilidade na interpreta\u00e7\u00e3o dos par\u00e2metros e aus\u00eancia de suposi\u00e7\u00f5es distribucionais. Contudo, a estrutura de associa\u00e7\u00e3o pode ser de interesse cient\u00edfico. Assim, modelos marginais de segunda ordem (GEE2) foram propostos na literatura com a introdu\u00e7\u00e3o de uma segunda equa\u00e7\u00e3o de estima\u00e7\u00e3o, o que possibilitou estimar e realizar infer\u00eancias para os par\u00e2metros de associa\u00e7\u00e3o a partir de covari\u00e1veis. Contudo, o m\u00e9todo GEE2 pode ser invi\u00e1vel computacionalmente se a quantidade de observa\u00e7\u00f5es dentro dos grupos for grande, o que limita sua aplica\u00e7\u00e3o em problemas pr\u00e1ticos. Como alternativa, foram propostos os m\u00e9todos ALR (Alternating Logistic Regression) e ORTH (Orthogonalized Residual). Ambos os enfoques especificam a associa\u00e7\u00e3o dentro de cada n\u00edvel de agrupamento em termos de raz\u00e3o de chances entre pares de resposta e compartilham a simplicidade computacional do m\u00e9todo GEE original.<br \/>Neste trabalhado, apresentamos e comparamos, via estudos de simula\u00e7\u00e3o, os m\u00e9todos ALR e ORTH. Probabilidades marginais e raz\u00f5es de chances s\u00e3o tamb\u00e9m estimadas e comparadas em um estudo ecol\u00f3gico envolvendo agrupamento hier\u00e1rquico de tr\u00eas n\u00edveis em que a presen\u00e7a ou aus\u00eancia de um grupo de fungos foi medida em diferentes locais de coleta no Brasil e Argentina.<\/p>\n<p>O trabalho publicado pode ser encontrado em<br \/>http:\/\/dx.doi.org\/10.1080\/02664763.2016.1238042<\/p>\n<p><\/div><\/p>\n<p><!-- \/wp:shortcode --><!-- wp:paragraph --><\/p>\n<p><!-- \/wp:paragraph --><!-- wp:group --><\/p>\n<p><!-- \/wp:group --><!-- wp:paragraph --><\/p>\n<p><!-- \/wp:paragraph --><!-- wp:group --><\/p>\n<div class=\"wp-block-group\">\n<p><!-- wp:shortcode --> <span class=\"collapseomatic \" id=\"id6ae263c7d86b2\"  tabindex=\"0\" title=\"Semin\u00e1rio 31\/08\/2020.  Prof Jean-David Fermanian(CREST\/Ensae - France)\"    >Semin\u00e1rio 31\/08\/2020.  Prof Jean-David Fermanian(CREST\/Ensae &#8211; France)<\/span><div id=\"target-id6ae263c7d86b2\" class=\"collapseomatic_content \"><\/p>\n<p><strong>T\u00edtulo: <\/strong>High dimensional penalized ARCH processes<\/p>\n<p style=\"text-align: left;\"><strong>Resumo<\/strong><strong>:<\/strong> We introduce a general methodology to consistently estimate multidimensional ARCH models equation-by-equation, possibly with a very large number of parameters through penalization (sparse group-lasso). Some families of multidimensional ARCH models are proposed to tackle homogeneous or heterogeneous portfolios of assets. The corresponding conditions of stationarity and of positive definiteness are studied. We evaluate the relevance of such a strategy by simulation. The relative forecasting performances of our models are compared through the management of financial portfolios.<\/p>\n<p><\/div> <!-- \/wp:shortcode --><\/p>\n<\/div>\n<p><!-- \/wp:group --><!-- wp:paragraph --><\/p>\n<p><!-- \/wp:paragraph --><!-- wp:shortcode --><\/p>\n<p><span class=\"collapseomatic \" id=\"id6ae263c7d86c3\"  tabindex=\"0\" title=\"Semin\u00e1rio 24\/08\/2020.  Dr. Kevin L. Keys (Ambys Medicine)\"    >Semin\u00e1rio 24\/08\/2020.  Dr. Kevin L. Keys (Ambys Medicine)<\/span><div id=\"target-id6ae263c7d86c3\" class=\"collapseomatic_content \"> <span lang=\"pt-BR\"><b>T\u00edtulo: <\/b><\/span>Cross-population generalizability of predictive models for gene expression<\/p>\n<p><div class=\"ast-oembed-container\" style=\"height: 100%;\"><iframe loading=\"lazy\" title=\"Semin\u00e1rio (24\/08\/2020)\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/_mHUWGgjElA?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 align=\"justify\"><span lang=\"pt-BR\"><b>Resumo: <\/b><\/span>Gene expression is a core component of human physiology. However, studies of genetically controlled gene expression and its effect on complex disease phenotypes have remained prohibitively expensive until recently. The advent of transcriptome-wide association studies (TWAS) arose concurrently with large datasets of linked genotype and RNA sequencing data, such as the Genotype-Tissue Expression Repository (GTEx) or the Depression Genes Network (DGN) dataset. TWAS leverages this paired genotype-expression information to create a powerful gene-based test of association. While TWAS has successfully identified dozens of novel genetic disease associations, recent interest has turned to possible sample biases in the reference data, which are overwhelmingly of European descent. Little is known about the generalizability of these models to other populations. We test for cross-population generalizability of gene expression prediction models using a dataset of African American individuals with whole blood expression data. We find that the default models trained in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction in prediction accuracy when compared to European Americans. We replicate these limitations in cross-population generalizability using the five populations in the GEUVADIS dataset. Via realistic simulations of both populations and gene expression, we show that accurate cross-population generalizability of transcriptome prediction only arises when eQTL architecture is substantially shared across populations. In contrast, models with non-identical eQTLs showed patterns similar to real-world data. These results point toward a pressing need to generate RNA-Seq data in multiple global human populations as a critical step towards multi-ethnic utility of gene expression prediction.<\/p>\n<p><\/div><\/p>\n<p><!-- \/wp:shortcode --><!-- wp:paragraph --><\/p>\n<p><!-- \/wp:paragraph --><!-- wp:group --><\/p>\n<div class=\"wp-block-group\">\n<p><!-- wp:shortcode --> <span class=\"collapseomatic \" id=\"id6ae263c7d86d0\"  tabindex=\"0\" title=\"Semin\u00e1rio 17\/08\/2020.  Prof\u00aa  Linda Lee Ho(USP)*\"    >Semin\u00e1rio 17\/08\/2020.  Prof\u00aa  Linda Lee Ho(USP)*<\/span><div id=\"target-id6ae263c7d86d0\" class=\"collapseomatic_content \"><\/p>\n<p class=\"v1MsoNormal\"><i><b>T\u00edtulo: \u00a0<\/b><\/i><span lang=\"EN-US\">Gr\u00e1ficos de controle por atributos no monitoramento de processo: algumas contribui\u00e7\u00f5es<\/span><\/p>\n<p><div class=\"ast-oembed-container\" style=\"height: 100%;\"><iframe loading=\"lazy\" title=\"Semin\u00e1rio (17\/08\/2020)\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/UHql_Uu1L3Y?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 class=\"v1MsoNormal\"><b><i>Resumo: <\/i><\/b><span lang=\"EN-US\">Tradicionalmente os gr\u00e1ficos de controle por atributos t\u00eam sido empregados para monitorar par\u00e2metros como fra\u00e7\u00e3o de n\u00e3o conformes (gr\u00e1ficos p, np) ou taxas de n\u00e3o conformidade (gr\u00e1ficos c ou u). O uso de gr\u00e1ficos de controle de atributos no monitoramento da m\u00e9dia e\/ou vari\u00e2ncia tem algumas vantagens, entre elas: o menor tempo e custo de inspe\u00e7\u00e3o, a n\u00e3o necessidade de tomar medidas das caracter\u00edsticas de qualidade em ensaios destrutivos.\u00a0 Este semin\u00e1rio tem o objetivo de apresentar as recentes contribui\u00e7\u00f5es de gr\u00e1ficos de controle por atributos utilizados para monitorar m\u00e9dia e\/ou vari\u00e2ncia.\u00a0<\/span><\/p>\n<p><\/div> <!-- \/wp:shortcode --><\/p>\n<\/div>\n<p><!-- \/wp:group --><!-- wp:paragraph --><\/p>\n<p><!-- \/wp:paragraph --><!-- wp:shortcode --><\/p>\n<p><span class=\"collapseomatic \" id=\"id6ae263c7d86ea\"  tabindex=\"0\" title=\"Semin\u00e1rio 10\/08\/2020.  Prof.  Wagner Barreto de Souza (UFMG)\"    >Semin\u00e1rio 10\/08\/2020.  Prof.  Wagner Barreto de Souza (UFMG)<\/span><div id=\"target-id6ae263c7d86ea\" class=\"collapseomatic_content \"><\/p>\n<p class=\"v1MsoNormal\"><b>T\u00edtulo:<\/b> A Negative Binomial Process with Composite Likelihood-Based Inference<\/p>\n<p class=\"v1MsoNormal\"><b>Resumo:<\/b> In this work, we propose a count time series model with negative binomial marginals for analyzing overdispersed data.\u00a0Properties of the proposed count model are\u00a0established. Our approach is to develop a method for inference based on a composite likelihood function. The strong consistency and the asymptotic normality of the maximum composite likelihood estimators are established. We derive a procedure for calculating the standard error of the parameter estimates and confidence intervals based on a parametric bootstrap. We also discuss prediction based on our negative binomial model and propose diagnostic tools based on the Probability Integral Transform histogram to check\u00a0model adequacy. Monte Carlo experiments were conducted to study and\u00a0compare the finite-sample properties of the proposed estimators. The simulations demonstrate that, compared to the generalized linear model approach combined with the ordinary least squares method,\u00a0the proposed composite likelihood approach provides satisfactory results for estimating the\u00a0parameters related to the correlation structure of the process, even under model misspecification. An application to real data is presented to illustrate the performance of our methodology in practical situations.<\/p>\n<p class=\"v1MsoNormal\"><i>This is joint work with Hernando Ombao (KAUST-Saudi Arabia).<\/i><\/p>\n<p><\/div><\/p>\n<p><span class=\"collapseomatic \" id=\"id6ae263c7d86f6\"  tabindex=\"0\" title=\"Semin\u00e1rio 03\/08\/2020.  Prof.  Francisco Cribari Neto (UFPE) *\"    >Semin\u00e1rio 03\/08\/2020.  Prof.  Francisco Cribari Neto (UFPE) *<\/span><div id=\"target-id6ae263c7d86f6\" class=\"collapseomatic_content \"><\/p>\n<p><strong>T\u00edtulo:<\/strong> Improved testing inferences for beta regressions with parametric mean link function<\/p>\n<p><div class=\"ast-oembed-container\" style=\"height: 100%;\"><iframe loading=\"lazy\" title=\"Semin\u00e1rio (03\/08\/2020)\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/AM-NBUrADLg?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><strong>Resumo:<\/strong> Beta regressions are widely used for modeling random variables that assume values in the standard unit interval, (0,1), such as rates, proportions, and income concentration indices. Parameter estimation is typically performed via maximum likelihood and hypothesis testing inferences on the model parameters are commonly performed using the likelihood ratio test. Such a test, however, may deliver inaccurate inferences when the sample size is small. It is thus important to develop alternative testing procedures that are more accurate when the sample contains only few observations. In this paper, we introduce the beta regression model with parametric mean link function and derive two modified likelihood ratio test statistics for that class of models. We provide simulation evidence that shows that the new tests usually outperform the standard likelihood ratio test in samples of small to moderate sizes. We also present and discuss two empirical applications.<\/p>\n<p><span data-sheets-formula-bar-text-style=\"font-size:15px;color:#000000;font-weight:bold;text-decoration:none;font-family:'docs-Calibri';font-style:italic;text-decoration-skip-ink:none;\">\u00a0<\/span><\/p>\n<p><\/div><\/p>\n<p><!-- \/wp:shortcode --><!-- wp:paragraph --><\/p>\n<p><!-- \/wp:paragraph --><!-- wp:group --><\/p>\n<p><!-- \/wp:group --><!-- wp:paragraph --><\/p>\n<p><!-- \/wp:paragraph --><!-- wp:shortcode --><\/p>\n<p><span class=\"collapseomatic \" id=\"id6ae263c7d8704\"  tabindex=\"0\" title=\"Semin\u00e1rio 27\/07\/2020.  Prof.  Marcelo Fernandes - FGV (EESP)* \"    >Semin\u00e1rio 27\/07\/2020.  Prof.  Marcelo Fernandes &#8211; FGV (EESP)* <\/span><div id=\"target-id6ae263c7d8704\" class=\"collapseomatic_content \"> <strong>T\u00edtulo:<\/strong> A semiparametric double-stochastic SIRD model with time-varying parameters.<\/p>\n<p><div class=\"ast-oembed-container\" style=\"height: 100%;\"><iframe loading=\"lazy\" title=\"Semin\u00e1rio (27\/07\/2020)\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/hzJu8D9qc2I?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 style=\"text-align: left;\"><strong>Resumo:<\/strong> We propose a semiparametric approach to estimate a stochastic SIRD model with time-varying parameters. We do not impose any restriction on daily transmission, recovering and mortality rates, instead using a fully nonparametric specification for their dynamics over time. To do so, we estimate the time-dependent SIRD model by kernel (constrained) least squares. We also entertain a multivariate extension to accommodate transmission across groups. Empirically, we estimate SIRD models for the COVID-19 cases and deaths in New York City. The results are very interesting unveiling a strong pattern of transmission across boroughs and age groups.<\/p>\n<p>This is joint work with Jos\u00e9 Antunes, Thiago Milagres and Alvaro Veiga. <\/div><\/p>\n<p><!-- \/wp:shortcode --><!-- wp:paragraph --><\/p>\n<p><!-- \/wp:paragraph --><!-- wp:group --><\/p>\n<div class=\"wp-block-group\">\n<p><!-- wp:shortcode --> <span class=\"collapseomatic \" id=\"id6ae263c7d8710\"  tabindex=\"0\" title=\"Semin\u00e1rio 20\/07\/2020.  Prof.  Hedibert Lopes *\"    >Semin\u00e1rio 20\/07\/2020.  Prof.  Hedibert Lopes *<\/span><div id=\"target-id6ae263c7d8710\" class=\"collapseomatic_content \"><\/p>\n<p class=\"western\"><span lang=\"pt-BR\"><b>T\u00edtulo: <\/b><\/span>Dynamic Sparsity on Dynamic Regression Models<span lang=\"pt-BR\">.<\/span><\/p>\n<p><div class=\"ast-oembed-container\" style=\"height: 100%;\"><iframe loading=\"lazy\" title=\"Semin\u00e1rio (20\/07\/2020)\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/8PYG89fD8jQ?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 class=\"western\" align=\"JUSTIFY\"><span lang=\"pt-BR\"><b>Resumo: <\/b><\/span><span style=\"color: #222222;\"><span style=\"font-family: Helvetica, serif;\"><span style=\"font-size: small;\">In the present work, we consider variable selection and shrinkage for the Gaussian dynamic linear regression\u00a0within a Bayesian framework. In particular, we propose a novel method that allows for time-varying\u00a0sparsity, based on an extension of spike-and-slab priors for dynamic models. This is done by\u00a0assigning appropriate Markov switching priors for the time-varying coefficients\u2019 variances, extending\u00a0the previous work of Ishwaran and Rao (2005).\u00a0 Furthermore, we investigate different priors, including\u00a0the common Inverted gamma prior for the process variances, and other mixture prior distributions such\u00a0as Gamma priors for both the spike and the slab, which leads to a mixture of Normal-Gammas priors\u00a0(Griffin et al., 2010) for the coefficients. In this sense, our prior can be viewed as a dynamic variable selection\u00a0prior which induces either smoothness (through the slab) or shrinkage towards zero (through the\u00a0spike) at each time point. The MCMC method used for posterior computation uses Markov latent variables\u00a0that can assume binary regimes at each time point to generate the coefficients\u2019 variances. In that\u00a0way, our model is a dynamic mixture model, thus, we could use the algorithm of Gerlach et al. (2000)\u00a0to generate the latent processes without conditioning on the states. Finally, our approach is exemplified\u00a0through simulated examples and a real data application.\u00a0 <\/span><\/span><\/span><\/p>\n<p class=\"western\" align=\"JUSTIFY\"><span style=\"color: #222222;\"><span style=\"font-family: Helvetica, serif;\"><span style=\"font-size: small;\">This is joint work with Paloma Uribe.<\/span><\/span><\/span><\/p>\n<p><\/div> <!-- \/wp:shortcode --><\/p>\n<\/div>\n<p><!-- \/wp:group --><!-- wp:paragraph --><\/p>\n<p><!-- \/wp:paragraph --><!-- wp:shortcode --><\/p>\n<p><span class=\"collapseomatic \" id=\"id6ae263c7d871f\"  tabindex=\"0\" title=\"Semin\u00e1rio 13\/07\/2020.  Prof. Marcelo Bourguignon Pereira* \"    >Semin\u00e1rio 13\/07\/2020.  Prof. Marcelo Bourguignon Pereira* <\/span><div id=\"target-id6ae263c7d871f\" class=\"collapseomatic_content \"><\/p>\n<p class=\"western\"><span lang=\"pt-BR\"><b>T\u00edtulo: <\/b><\/span>Modeling count time series with equidispersion, underdispersion or overdispersion<span lang=\"pt-BR\">.<\/span><\/p>\n<p><div class=\"ast-oembed-container\" style=\"height: 100%;\"><iframe loading=\"lazy\" title=\"Seminario (13\/07\/2020)\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/lYqEw_VZqV4?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 class=\"western\" align=\"JUSTIFY\"><span lang=\"pt-BR\"><b>Resumo: <\/b><\/span>We present a novel first-order nonnegative integer-valued autoregressive model for stationary count data processes with Bernoulli-geometric marginals based on a new type of generalized thinning operator. It can be used for modeling time series of counts with equidispersion, underdispersion and overdispersion. The main properties of the model are derived, such as probability generating function, moments, transition probabilities and zero probability. The maximum likelihood method is used for estimating the model parameters. The proposed model is fitted to time series of counts of iceberg orders and of cases of family violence illustrating its capabilities in challenging cases of overdispersed and equidispersed count data<span lang=\"pt-BR\">.<\/span><\/p>\n<p><\/div><\/p>\n<p><!-- \/wp:shortcode --><!-- wp:paragraph --><\/p>\n<p><!-- \/wp:paragraph --><!-- wp:group --><\/p>\n<div class=\"wp-block-group\">\n<p><!-- wp:shortcode --> <span class=\"collapseomatic \" id=\"id6ae263c7d872b\"  tabindex=\"0\" title=\"Semin\u00e1rio 06\/07\/2020.  Prof\u00aa Silvana Schneider \"    >Semin\u00e1rio 06\/07\/2020.  Prof\u00aa Silvana Schneider <\/span><div id=\"target-id6ae263c7d872b\" class=\"collapseomatic_content \"><\/p>\n<p class=\"western\"><span lang=\"pt-BR\"><b>T\u00edtulo: <\/b><\/span><span lang=\"pt-BR\">Abordagens cl\u00e1ssica e bayesiana para dados de sobreviv\u00eancia com censura dependente.<\/span><\/p>\n<p class=\"western\" align=\"JUSTIFY\"><span lang=\"pt-BR\"><b>Resumo: <\/b><\/span><span lang=\"pt-BR\">Normalmente, na an\u00e1lise de sobreviv\u00eancia 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, especificamente a abordagem proposta por Huang &amp; Wolfe (2002). Para tal, apresentamos uma vers\u00e3o completamente param\u00e9trica, utilizando a distribui\u00e7\u00e3o Weibull para ajustar os tempos de falha e censura; uma vers\u00e3o semiparam\u00e9trica, considerando a distribui\u00e7\u00e3o Exponencial por Partes para modelar os tempos; apresentamos as infer\u00eancias sob enfoque cl\u00e1ssico e bayesiano. Para avaliar os modelos 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, para dados de sobreviv\u00eancia multivariados. 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).<\/span><\/p>\n<p><\/div> <!-- \/wp:shortcode --><\/p>\n<\/div>\n<p><!-- \/wp:group --><!-- wp:paragraph --><\/p>\n<p><!-- \/wp:paragraph --><!-- wp:shortcode --><\/p>\n<p><span class=\"collapseomatic \" id=\"id6ae263c7d8737\"  tabindex=\"0\" title=\"Semin\u00e1rio 29\/06\/2020.  Prof\u00aa Taiane Schaedler Prass* \"    >Semin\u00e1rio 29\/06\/2020.  Prof\u00aa Taiane Schaedler Prass* <\/span><div id=\"target-id6ae263c7d8737\" class=\"collapseomatic_content \"> <\/p>\n<p>\u00a0<\/p>\n<p>T\u00edtulo:\u00a0<strong>Propriedades da DFA e da DCCA em processos do tipo trend-stationary e o pacote DCCA do R<\/strong><\/p>\n<p><div class=\"ast-oembed-container\" style=\"height: 100%;\"><iframe loading=\"lazy\" title=\"Semin\u00e1rio (30\/06\/2020)\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/_-DGDNepTno?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><strong>Resumo<\/strong>: Processos trend-stationary s\u00e3o aqueles que podem ser escritos como a soma de um processo estacion\u00e1rio mais uma tend\u00eancia polinomial. Tais processo s\u00e3o, obviamente, n\u00e3o estacion\u00e1rios e estat\u00edsticas b\u00e1sicas como vari\u00e2ncia, autocorrela\u00e7\u00e3o, correla\u00e7\u00e3o cruzada, entre outras, perdem seu significado tradicional. Nesse contexto, a an\u00e1lise de flutua\u00e7\u00e3o destendenciada (Detrended Fluctuation Analysis &#8211; DFA) e a an\u00e1lise de correla\u00e7\u00e3o cruzada destendenciada (Detrended Cross-Correlation Analysis &#8211; DCCA) aparecem como alternativas para investigar o comportamento marginal e conjunto desses processos. A DFA \u00e9 heuristicamente descrita como uma maneira indireta de quantificar variabilidade em s\u00e9ries temporais n\u00e3o estacion\u00e1rias. A DCCA \u00e9 uma generaliza\u00e7\u00e3o do DFA para o contexto em que o interesse reside no comportamento conjunto de um par de s\u00e9ries temporais. O coeficiente de correla\u00e7\u00e3o cruzada (\ud835\udf0c\ud835\udc37\ud835\udc36\ud835\udc36\ud835\udc34) combina essas duas an\u00e1lises de forma a produzir um coeficiente que \u00e9 visto como um quantificador indireto da correla\u00e7\u00e3o cruzada. Tais quantidades s\u00e3o calculadas considerando-se janelas de diferentes tamanhos para os dados e o estudo do decaimento dos coeficientes, conforme o tamanho da janela aumenta, permite obter conclus\u00f5es sobre o decaimento das fun\u00e7\u00f5es de autocovari\u00e2ncia e\/ou covari\u00e2ncia cruzada dos processos em quest\u00e3o. Neste semin\u00e1rio apresentamos alguns resultados derivados no artigo On the behavior of the DFA and DCCA in trend-stationary processes (Prass e Pumi, 2020 \u2013 under revision), relacionados a DFA e DCCA, para processos do tipo trend-stationary. Entre os resultados discute-se a estacionariedade dos coeficientes vistos como processos estoc\u00e1sticos e a consist\u00eancia dos mesmos, quando vistos como estimadores amostrais. Os resultados ser\u00e3o ilustrados via exemplos simulados e uma aplica\u00e7\u00e3o \u00e0 dados reais. Os c\u00e1lculos ser\u00e3o ilustrados com a utiliza\u00e7\u00e3o do pacote DCCA do R, desenvolvido pelos autores. <\/div><\/p>\n<p><!-- \/wp:shortcode --><\/p>\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":"","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\/681"}],"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=681"}],"version-history":[{"count":7,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/pages\/681\/revisions"}],"predecessor-version":[{"id":764,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/pages\/681\/revisions\/764"}],"wp:attachment":[{"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/media?parent=681"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}