{"id":2813,"date":"2026-08-07T12:34:29","date_gmt":"2026-08-07T15:34:29","guid":{"rendered":"https:\/\/www.ufrgs.br\/ppgest\/?page_id=2813"},"modified":"2026-08-27T11:14:26","modified_gmt":"2026-08-27T14:14:26","slug":"2026-2","status":"publish","type":"page","link":"https:\/\/www.ufrgs.br\/ppgest\/2026-2\/","title":{"rendered":"2026"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"2813\" class=\"elementor elementor-2813\">\n\t\t\t\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ee3419d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ee3419d\" 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-b681dd0\" data-id=\"b681dd0\" 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-6c45fa6 elementor-widget elementor-widget-text-editor\" data-id=\"6c45fa6\" 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<h2 data-elementor-setting-key=\"title\" data-pen-placeholder=\"Type Here...\" style=\"font-style: normal; margin: 0px; color: rgb(51, 65, 85);\">2026<\/h2>\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<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c891cc0 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c891cc0\" 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-69f3ca0\" data-id=\"69f3ca0\" 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-169f3b1 elementor-widget elementor-widget-text-editor\" data-id=\"169f3b1\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span dir=\"auto\" style=\"vertical-align: inherit;\"><span class=\"collapseomatic \" id=\"id6ae5cdf6e33b7\"  tabindex=\"0\" title=\"Semin\u00e1rio 16\/06\/2026 - Profa. Dra. Fernanda Muller (UFRGS)\"    >Semin\u00e1rio 16\/06\/2026 &#8211; Profa. Dra. Fernanda Muller (UFRGS)<\/span><div id=\"target-id6ae5cdf6e33b7\" class=\"collapseomatic_content \"><\/span><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span style=\"font-weight: bold;\"><span dir=\"auto\" style=\"vertical-align: inherit;\">T\u00edtulo:<\/span><\/span><span dir=\"auto\" style=\"vertical-align: inherit;\"> Portfolio Optimization through risk-averse linear models.<\/span><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400; text-align: justify;\"><span style=\"font-weight: bold;\"><span dir=\"auto\" style=\"vertical-align: inherit;\">Resumo: <\/span><\/span>We establish a novel framework for minimum deviation portfolio optimization by directly connecting a risk-averse stochastic problem (RASP) to linear regression models. By leveraging a set of coherent risk measures and scoring functions, our methodology generalizes classical models while enabling alternative formulations that account for tail risk and asymmetric scoring functions. Using S&amp;P 100 stock data, we empirically illustrate our approach. Nonparametric hypothesis tests indicate significant differences in risk and risk-adjusted performance across RASP specifications.<\/p>\n<p style=\"text-align: justify;\">Keywords: Portfolio optimization; Risk management; Risk measures; Deviation measures; Regression models.<\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><\/div><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span dir=\"auto\" style=\"vertical-align: inherit;\"><span class=\"collapseomatic \" id=\"id6ae5cdf6e33f3\"  tabindex=\"0\" title=\"Semin\u00e1rio 23\/06\/2026 - Profa. Dra. Denise Duarte (UFMG)\"    >Semin\u00e1rio 23\/06\/2026 &#8211; Profa. Dra. Denise Duarte (UFMG)<\/span><div id=\"target-id6ae5cdf6e33f3\" class=\"collapseomatic_content \"><\/span><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span style=\"font-weight: bold;\"><span dir=\"auto\" style=\"vertical-align: inherit;\">T\u00edtulo: <\/span><\/span><span dir=\"auto\" style=\"vertical-align: inherit;\">Modelos de Redes de Afinidade.<\/span><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400; text-align: justify;\"><span style=\"font-weight: bold;\"><span dir=\"auto\" style=\"vertical-align: inherit;\">Resumo: <\/span><\/span><span dir=\"auto\" style=\"vertical-align: inherit;\">Este trabalho apresenta o Modelo de Rede de Afinidade para grafos aleat\u00f3rios, que engloba uma ampla fam\u00edlia de modelos de grafos aleat\u00f3rios parametrizados. Neste modelo, assumimos que cada indiv\u00edduo escolhe aleatoriamente um conjunto de caracter\u00edsticas para represent\u00e1-lo, de acordo com uma medida de probabilidade espec\u00edfica. As conex\u00f5es entre dois indiv\u00edduos dependem de suas caracter\u00edsticas compartilhadas e s\u00e3o ponderadas por uma fun\u00e7\u00e3o que mede o que chamamos de afinidade na rede. Dependendo da escolha dessa fun\u00e7\u00e3o, a densidade da rede pode variar de esparsa a um grafo completo. Essa alta flexibilidade torna o modelo adequado para ajustar redes do mundo real. Desenvolvemos uma metodologia para estimar os par\u00e2metros do Modelo de Rede de Afinidade. Al\u00e9m disso, estendemos este modelo para acomodar redes que abrangem m\u00faltiplas comunidades. Finalmente, aplicamos o modelo para analisar as diferen\u00e7as na percep\u00e7\u00e3o da pandemia de COVID-19 entre dois grupos: uma comunidade acad\u00eamica e uma comunidade externa.<\/span><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><\/div><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span dir=\"auto\" style=\"vertical-align: inherit;\"><span class=\"collapseomatic \" id=\"id6ae5cdf6e3408\"  tabindex=\"0\" title=\"Semin\u00e1rio 30\/06\/2026 - Profa. Dra. Cibele M. Russo (USP)\"    >Semin\u00e1rio 30\/06\/2026 &#8211; Profa. Dra. Cibele M. Russo (USP)<\/span><div id=\"target-id6ae5cdf6e3408\" class=\"collapseomatic_content \"><\/span><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span style=\"font-weight: bold;\"><span dir=\"auto\" style=\"vertical-align: inherit;\">T\u00edtulo: <\/span><\/span><span dir=\"auto\" style=\"vertical-align: inherit;\">Modelos n\u00e3o-lineares para dados correlacionados: uma vis\u00e3o geral e alguns desafios.<\/span><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400; text-align: justify;\"><span style=\"font-weight: bold;\"><span dir=\"auto\" style=\"vertical-align: inherit;\">Resumo:&nbsp;<\/span><\/span><span dir=\"auto\" style=\"vertical-align: inherit;\"> Para dados correlacionados com comportamento n\u00e3o-linear, como curvas de crescimento e farmacocin\u00e9ticas, os modelos n\u00e3o-lineares com efeitos mistos representam alternativas competitivas, por\u00e9m com desafios desde a converg\u00eancia de algoritmos at\u00e9 a an\u00e1lise de diagn\u00f3stico. Neste semin\u00e1rio, discutiremos teoria e aplica\u00e7\u00f5es de modelos n\u00e3o-lineares para dados correlacionados, desde m\u00e9todos de estima\u00e7\u00e3o de par\u00e2metros, an\u00e1lise de diagn\u00f3stico, testes de hip\u00f3teses para componentes de vari\u00e2ncia, varia\u00e7\u00f5es nas suposi\u00e7\u00f5es dos modelos e alguns dos desafios e oportunidades nessa linha de pesquisa.<\/span><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><\/div><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span class=\"collapseomatic \" id=\"id6ae5cdf6e341e\"  tabindex=\"0\" title=\"Semin\u00e1rio 07\/07\/2026 - Prof. Dr. Fl\u00e1vio Augusto Ziegelmann (UFRGS)\"    >Semin\u00e1rio 07\/07\/2026 &#8211; Prof. Dr. Fl\u00e1vio Augusto Ziegelmann (UFRGS)<\/span><div id=\"target-id6ae5cdf6e341e\" class=\"collapseomatic_content \"><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span style=\"font-weight: bold;\"><span dir=\"auto\" style=\"vertical-align: inherit;\">T\u00edtulo: <\/span><\/span><span dir=\"auto\" style=\"vertical-align: inherit;\">Detecting Changes in Production Frontiers.<\/span><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400; text-align: justify;\"><span style=\"font-weight: bold;\"><span dir=\"auto\" style=\"vertical-align: inherit;\">Resumo:&nbsp;<\/span><\/span><span dir=\"auto\" style=\"vertical-align: inherit;\"> We study the problem of estimating locations in time at which the level of technology in an economy changes when given a sequence of time ordered inputs and outputs. We approach the problem through the lens of nonparametric frontier analysis with frontiers that expand sharply and globally over time, and develop an offline change point detection procedure. Theory, simulation studies and real data examples are presented.<\/span><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><\/div><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span class=\"collapseomatic \" id=\"id6ae5cdf6e342d\"  tabindex=\"0\" title=\"Semin\u00e1rio 14\/07\/2026 - Profa. Dra. Daiane Aparecida Zuanetti (PIPGEs \u2013 UFSCar-USP)\"    >Semin\u00e1rio 14\/07\/2026 &#8211; Profa. Dra. Daiane Aparecida Zuanetti (PIPGEs \u2013 UFSCar-USP)<\/span><div id=\"target-id6ae5cdf6e342d\" class=\"collapseomatic_content \"><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span style=\"font-weight: bold;\"><span dir=\"auto\" style=\"vertical-align: inherit;\">T\u00edtulo: <\/span><\/span><span dir=\"auto\" style=\"vertical-align: inherit;\">Identifying topics in text using parametric and non-parametric Bayesian models.<\/span><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400; text-align: justify;\"><span style=\"font-weight: bold;\"><span dir=\"auto\" style=\"vertical-align: inherit;\">Resumo:&nbsp;<\/span><\/span><span dir=\"auto\" style=\"vertical-align: inherit;\"> One branch within text analysis is topic modeling, whose methodologies aim to understand the topic structure that forms a document and segmenting multiple documents by their dominant topics (subjects). One of the pioneering methods in this context is the mixture model (MM), which assumes that each document is composed of words from a single topic. Given this limitation, the methodology of latent Dirichlet allocation (LDA) has gained considerable visibility due to its greater flexibility. The number of topics needs to be predetermined for both MM and LDA estimation, and if unknown, the best number can be chosen via selection criteria. One alternative for selecting the number of topics and estimating the model simultaneously is through non-parametric Bayesian methodologies. In this work, in addition to contrasting probabilistic methodologies for topic analysis and proposing a non-parametric marginal Bayesian mixture model for this purpose, we compare criteria that generally measure the quality of the models and are used for choosing the number of topics. To do this, we apply the models and selection criteria to two sets of real data. Joint work with Edvaldo Capobiango Coelho Filho.<\/span><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><\/div><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span class=\"collapseomatic \" id=\"id6ae5cdf6e343f\"  tabindex=\"0\" title=\"Semin\u00e1rio 21\/07\/2026 - Prof. Dr.&nbsp;Sharandeep Singh Pandher (University of Alberta \u2013 Canada)\"    >Semin\u00e1rio 21\/07\/2026 &#8211; Prof. Dr.&nbsp;Sharandeep Singh Pandher (University of Alberta \u2013 Canada)<\/span><div id=\"target-id6ae5cdf6e343f\" class=\"collapseomatic_content \"><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span style=\"font-weight: bold;\"><span dir=\"auto\" style=\"vertical-align: inherit;\">T\u00edtulo: <\/span><\/span><span dir=\"auto\" style=\"vertical-align: inherit;\">Improving the Efficiency of Parameter Estimation in \u03b2ARMA Models Using Pretest and Shrinkage Estimators.<\/span><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400; text-align: justify;\"><span style=\"font-weight: bold;\"><span dir=\"auto\" style=\"vertical-align: inherit;\">Resumo: <\/span><\/span>Beta regression models are widely used for analyzing time series data bounded within the interval (0,1), such as proportions and rates. Although the beta autoregressive moving average (\u03b2ARMA) model effectively captures temporal dependence, the maximum likelihood estimator (MLE) may exhibit limited efficiency, particularly in small samples. This study investigates the use of pretest and shrinkage estimators for \u03b2ARMA models. Specifically, we adapt these estimators to shrink the MLE toward a pre-specified target, thereby reducing estimation variability. Through extensive Monte Carlo simulation studies under various sample sizes and autocorrelation structures, we demonstrate that the proposed estimators consistently outperform the MLE in terms of mean squared error, particularly when the true parameter values are close to the shrinkage target. An application to a real-world dataset further illustrates the practical advantages of the proposed approach.<\/p>\n<p style=\"text-align: justify;\">Keywords: \u03b2ARMA model; maximum likelihood estimation; Monte Carlo simulation; pretest estimation; shrinkage estimation; asymptotic distributional bias; asymptotic risk.<\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><\/div><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span class=\"collapseomatic \" id=\"id6ae5cdf6e344d\"  tabindex=\"0\" title=\"Semin\u00e1rio 28\/07\/2026 - Profa. Dra. M\u00e1rcia Brand\u00e3o de Oliveira Martins (UFAM)\"    >Semin\u00e1rio 28\/07\/2026 &#8211; Profa. Dra. M\u00e1rcia Brand\u00e3o de Oliveira Martins (UFAM)<\/span><div id=\"target-id6ae5cdf6e344d\" class=\"collapseomatic_content \"><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span style=\"font-weight: bold;\"><span dir=\"auto\" style=\"vertical-align: inherit;\">T\u00edtulo: <\/span><\/span><span dir=\"auto\" style=\"vertical-align: inherit;\">A Poisson\u2013Birnbaum\u2013Saunders Mixture Cure Model: An Application to Breast Cancer Data.<\/span><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400; text-align: justify;\"><span style=\"font-weight: bold;\"><span dir=\"auto\" style=\"vertical-align: inherit;\">Resumo: <\/span><\/span><span dir=\"auto\" style=\"vertical-align: inherit;\">We introduce a new modelling for long-term survival models, assuming that the number of competing causes follows a mixture of Poisson and the Birnbaum-Saunders distribution. In this context, we present some statistical properties of our model and demonstrate that the promotion time model emerges as a limiting case. We delve into detailed discussions of pecific models within this class. Notably, we examine the expected number of competing causes, which depends on covariates. This allows for direct modeling of the cure rate as a function of covariates.We present an Expectation-Maximization (EM) algorithm for parameter estimation, to discuss the estimation via maximum likelihood (ML) and provide insights into parameter inference for this model. Additionally, we outline sufficient conditions for ensuring the consistency and asymptotic normal distribution of ML estimators. To evaluate the performance of our estimation method, we conduct a Monte Carlo simulation to provide asymptotic properties and a power study of LR test by contrasting our methodology against the promotion time model. To demonstrate the practical applicability of our model, we apply it to a real medical dataset from a population-based study of incidence of breast cancer in S\u00e3o Paulo, Brazil. Our results illustrate that the proposed model can outperform traditional approaches in terms of model fitting, highlighting its potential utility in real-world scenarios.<\/span><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><\/div><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span class=\"collapseomatic \" id=\"id6ae5cdf6e346f\"  tabindex=\"0\" title=\"Semin\u00e1rio 04\/08\/2026 -&nbsp; Profa. Dra. Leila Amorim (UFBA)\"    >Semin\u00e1rio 04\/08\/2026 &#8211;&nbsp; Profa. Dra. Leila Amorim (UFBA)<\/span><div id=\"target-id6ae5cdf6e346f\" class=\"collapseomatic_content \"><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span style=\"font-weight: bold;\"><span dir=\"auto\" style=\"vertical-align: inherit;\">T\u00edtulo: <\/span><\/span><span dir=\"auto\" style=\"vertical-align: inherit;\">Extending Causal Mediation Analysis to Categorical Latent Variables.<\/span><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400; text-align: justify;\"><span style=\"font-weight: bold;\"><span dir=\"auto\" style=\"vertical-align: inherit;\">Resumo: <\/span><\/span><span dir=\"auto\" style=\"vertical-align: inherit;\">Elucidating the mechanisms and causal pathways underlying associations among variables is fundamental to the advancement of scientific knowledge and the rigorous investigation of complex phenomena. Within the causal mediation framework, the total effect of an intervention on an outcome is typically decomposed into the natural direct effect (NDE) and the natural indirect effect (NIE). While a substantial body of literature addresses mediation analysis, comparatively little attention has been given to settings in which key variables are latent and categorical, as defined through latent class analysis (LCA). In this work, we extend existing methodologies for the estimation of causal mediation effects by explicitly incorporating latent components into the analytical framework. The performance of the proposed approaches is evaluated via simulation studies, encompassing both regression-based models and marginal structural models, under scenarios in which the mediator and\/or the outcome is latent. An empirical application is provided using data from an intervention aimed at promoting healthy behaviors among high school students in Brazil. The proposed methodological developments have broad applicability and may be readily extended to a wide range of substantive research contexts.<\/span><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><\/div><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span class=\"collapseomatic \" id=\"id6ae5cdf6e3485\"  tabindex=\"0\" title=\"Semin\u00e1rio 13\/08\/2026 -&nbsp; Profa. Dra. Clarice Garcia Borges Dem\u00e9trio (ESALQ\/USP)\"    >Semin\u00e1rio 13\/08\/2026 &#8211;&nbsp; Profa. Dra. Clarice Garcia Borges Dem\u00e9trio (ESALQ\/USP)<\/span><div id=\"target-id6ae5cdf6e3485\" class=\"collapseomatic_content \"><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span style=\"font-weight: bold;\"><span dir=\"auto\" style=\"vertical-align: inherit;\">T\u00edtulo: <\/span><\/span><span dir=\"auto\" style=\"vertical-align: inherit;\">Discrete Survival Models for Biological Pest Control.<\/span><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400; text-align: justify;\"><span style=\"font-weight: bold;\"><span dir=\"auto\" style=\"vertical-align: inherit;\">Resumo: <\/span><\/span><span dir=\"auto\" style=\"vertical-align: inherit;\">In many experimental settings, event times are recorded at discrete intervals, e.g. days, and can be viewed as a form of longitudinal repeated-measurement data with the outcome on each day being death or survival. Here we consider such data for groups of individuals, specifically groups of the termite Heterotermes tenuis treated by different isolates of the fungus Beauveria bassiana with 5 replicates of each isolate. The aim is to identify the effective isolates, characterised for example by short LT50s, and also to look for reliable isolates with small replicate variability. We introduce a family of discrete survival models that can incorporate both proportional and additive hazards to describe the mortality history of the groups of termites. These models include an arbitrary piecewise constant baseline hazard and additional parameters are used to characterise the efficacy of the different isolates. Random effect versions of these models are also used to account for the appreciable isolate and replicate variation. In addition, we make links between ordered multinomial models (with ordered categories given by days) and the discrete survival approach using conditional responses over time for the deaths from the number at risk in each time interval, a form of sequential binomial model. In the discrete survival models, including time as a factor gives a baseline discrete hazard function. The ordered multinomial models do not include this and try to capture this time dependence through terms for the ordered time, such as a simple trend. Fitting a continuation ratio sequential binomial model without the time factor and just a simple time trend gives comparable results to the ordered multinomial. In addition, we have explored using different link functions, including logit, cloglog, and clog, for the ordered multinomial models and the discrete survival models, giving very little difference in the results. Different link functions are more interesting in the discrete survival models, where they have an interpretation in terms of the hazards. We can replace the binomial by the quasi-binomial or beta-binomial, and the estimated dispersions are similar to those from an ordered Dirichlet multinomial model. The fitted models can be used to summarise the effectiveness and reliability of the isolates. Furthermore, by using a non-parametric distribution for the isolate random effect, a more formal clustering of similar isolates can be obtained. The models can also be extended to include time-dependent isolate effects, allowing for a more general classification of the efficacy and virulence of the isolates. This study was partially financed by the S\u00e3o Paulo Research Foundation (FAPESP), Brazil. Project Numbers: 2022\/11865-1 and 2024\/01638-3. Trabalho em conjunto com: Lida Fallah (TU Dublin, Ireland), Silvia Maria de Freitas (UFC, CE, Brazil) e John Hinde (University of Galway, Ireland).<\/span><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><\/div><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span class=\"collapseomatic \" id=\"id6ae5cdf6e34b0\"  tabindex=\"0\" title=\"Semin\u00e1rio 27\/08\/2026 -&nbsp; Prof. Dr. Rodrigo dos Santos Targino (FGV-RJ)\"    >Semin\u00e1rio 27\/08\/2026 &#8211;&nbsp; Prof. Dr. Rodrigo dos Santos Targino (FGV-RJ)<\/span><div id=\"target-id6ae5cdf6e34b0\" class=\"collapseomatic_content \"><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span style=\"font-weight: bold;\"><span dir=\"auto\" style=\"vertical-align: inherit;\">T\u00edtulo: <\/span><\/span><span dir=\"auto\" style=\"vertical-align: inherit;\">Stochastic modelling of football matches using dynamic regressors.<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-size: 16px; font-style: normal; font-weight: bold;\"><span dir=\"auto\" style=\"vertical-align: inherit;\">Resumo:&nbsp;<\/span><\/span>This paper develops a general framework for stochastic modeling of goals and other events in football (soccer) matches. The events are modelled as Cox processes (doubly stochastic Poisson processes) where the event intensities may depend on all the modeled events as well as external factors. The model has a strictly concave log-likelihood function which facilitates its fitting to observed data. Besides event times, the model describes the random lengths of stoppage times which can have a strong influence on the final score of a match. The model is illustrated on eight years of data from Campeonato Brasileiro de Futebol S\u00e9rie A. We find that dynamic regressors significantly improve the in-game predictive power of the model. In particular, a) when a team receives a red card, its goal intensity decreases more than 30%; b) the goal scoring rate of a team increases by 10% if it is losing by one goal and by 20% if its losing by two goals; and c) when the goal difference at the end of the second half is less than or equal to one, the stoppage time is on average more than one minute longer than in matches with a difference of two or more goals.<\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><\/div><\/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<section class=\"elementor-section elementor-top-section elementor-element elementor-element-34c0499 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"34c0499\" 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-39cda67\" data-id=\"39cda67\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\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>2026<\/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":"no-sidebar","site-content-layout":"page-builder","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":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"disabled","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\/2813"}],"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=2813"}],"version-history":[{"count":22,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/pages\/2813\/revisions"}],"predecessor-version":[{"id":2903,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/pages\/2813\/revisions\/2903"}],"wp:attachment":[{"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/media?parent=2813"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}