Seminários do DEST


05/03/2021 às  14:30 hs - Local: Canal do Youtube: Video conferencia do DEST 

Larissa Natany Almeida Martins (Doutoranda - DEST/UFMG)

Título: A Bayesian network approach for population synthesis.

Resumo: Agent-based micro-simulation models require a complete list of agents with detailed demographic/socioeconomic information for the purpose of behavior modeling and simulation. This paper introduces a new alternative for population synthesis based on Bayesian networks. A Bayesian network is a graphical representation of a joint probability distribution, encoding probabilistic relationships among a set of variables in an efficient way. Similar to the previously developed probabilistic approach, in this paper, we consider the population synthesis problem to be the inference of a joint probability distribution. In this sense, the Bayesian network model becomes an efficient tool that allows us to compactly represent/reproduce the structure of the population system and preserve privacy and confidentiality in the meanwhile. We demonstrate and assess the performance of this approach in generating synthetic population for Singapore, by using the Household Interview Travel Survey (HITS) data as the known test population. Our results show that the introduced Bayesian network approach is powerful in characterizing the underlying joint distribution, and meanwhile the overfitting of data can be avoided as much as possible.

05/03/2021 às  13:30 hs - Local: Canal do Youtube: Video conferencia do DEST 

Marcio Augusto Ferreira Rodrigues (Doutorando - DEST/UFMG)

Título: Semiparametric regression analysis of interval-censored competing risks data.

Resumo: Interval-censored competing risks data arise when each study subject may experience an event or failure from one of several causes and the failure time is not observed directly but rather is known to lie in an interval between two examinations. We formulate the effects of possibly time-varying (external) covariates on the cumulative incidence or sub-distribution function of competing risks (i.e., the marginal probability of failure from a specific cause) through a broad class of semiparametric regression models that captures both proportional and non-proportional hazards structures for the sub-distribution. We allow each subject to have an arbitrary number of examinations and accommodate missing information on the cause of failure. We consider nonparametric maximum likelihood estimation and devise a fast and stable EM-type algorithm for its computation. We then establish the consistency, asymptotic normality, and semiparametric efficiency of the resulting estimators for the regression parameters by appealing to modern empirical process theory. In addition, we show through extensive simulation studies that the proposed methods perform well in realistic situations. Finally, we provide an application to a study on HIV-1 infection with different viral subtypes.

26/02/2021 às  13:30 hs - Local: Canal do Youtube: Video conferencia do DEST 

Ana Júlia Alves Câmara (Doutoranda - DEST/UFMG)

Título: On generalized additive models with dependent time series covariate.

Resumo: The generalized additive model (GAM) is a standard statistical methodology and is frequently used in various fields of applied data analysis where the response variable is non-normal, e.g., integer valued, and the explanatory variables are continuous, typically normally distributed. Standard assumptions of this model, among others, are that the explanatory variables are independent and identically distributed vectors which are not multicollinear. To handle the multicollinearity and serial dependence together a new hybrid model, called GAM-PCA-VAR model, was proposed in [17] which is the combination of GAM with the principal component analysis (PCA) and the vector autoregressive (VAR) model. In this paper, some properties of the GAM-PCA-VAR model are discussed theoretically and verified by simulation. A real data
set is also analysed with the aim to describe the association between respiratory disease and air pollution concentrations.

19/02/2021 às  14:30 hs - Local: Canal do Youtube: Video conferencia do DEST 

Marta Cristina Colozza Bianchi (Doutoranda - DEST/UFMG)

Título: Modelos de mistura com dependência Markoviana para identificar observações atípicas em série temporal com espaçamento irregular.

Resumo: Neste seminário serão apresentados dois modelos Bayesianos de mistura com dependência Markoviana. A modelagem é motivada por duas aplicações para análise de milhares de medições de expressão gênica, em tumores de alguns tipos de câncer, cujas localizações são mapeadas em cromossomos definindo séries com espaçamento irregular. Este tipo de modelo foi proposto em Mayrink e Gonçalves (2017) com aplicação a dados de microarray, e estendido em Mayrink e Gonçalves (2020) com aplicação a dados RNA-Seq. Em ambos os estudos, o objetivo é identificar observações atípicas. No contexto de microarrays, deseja-se detectar regiões genômicas associadas a valores de alta expressão (superexpressão), que definem clusters de observações consecutivas. Já na análise de RNA-Seq, o objetivo é encontrar dois tipos de regiões cromossômicas: superexpressão e subexpressão. As características de alta ou baixa expressão gênica são importantes para estudar a progressão de um câncer. Através delas identificam-se regiões contendo genes com atividade diferenciada na doença. Em Mayrink e Gonçalves (2017) o modelo desenvolvido considera uma mistura de distribuições com médias ordenadas de forma que o último componente seja responsável por acomodar genes superexpressos. No trabalho de 2020, o primeiro e último componentes da mistura incorporam os genes subexpressos e superexpressos, respectivamente. O modelo é flexível o suficiente para lidar de forma eficiente com o espaçamento irregular dos dados ao usar as informações de distância entre expressões vizinhas para inferir sobre a existência de uma dependência Markoviana. Esta dependência tem papel chave para a detecção das regiões de interesse. A inferência Bayesiana é realizada por meio de amostragem indireta via algoritmos MCMC.

19/02/2021 às  13:30 hs - Local: Canal do Youtube: Video conferencia do DEST 

Magda Carvalho Pires - DEST/UFMG (Joint work with Milena S. Marcolino, Lucas E. F. Ramos, Rafael T. Silva, Luana M. Oliveira

Título: ABC2-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison with other available scores.


Coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 virus, is still the main global health, social and economic challenge. In this context, fast and efficient assessment of prognosis of the disease is needed to optimize the allocation of health care and human resources, to empower early identification and intervention of patients at higher risk of poor outcome. Thus, rapid scoring systems, which combine different variables to estimate the risk of poor outcome, may be extremely helpful for fast and effective assessment of those patients in the emergency department. Following international guidelines, generalized additive models and LASSO logistic regression were performed to develop a prediction model for in-hospital mortality, based on the 3978 patients that were admitted during March-July, 2020. The model was validated in the 1054 patients admitted during August-September 30, as well as in an external cohort of 474 Spanish patients. Our ABC2-SPH score showed good discrimination, calibration and overall performance in both Brazilian cohorts, but, in the Spanish cohort, mortality was somewhat underestimated in patients with very high (>25%) risk. The ABC2-SPH score is implemented in a freely available online risk calculator (


05/02/2021 às  13:30 hs - Local: Canal do Youtube: Video conferencia do DEST 

Joseph Lucas (Senior Research Scientist na Caravan Health, EUA)

Título: A practical guide to prediction using temporal event data.


We look at some practical aspects to prediction using (potentially high dimensional) temporal event data. The talk will touch on (i) feature extraction, (ii) overfitting, (iii) using a model agnostic approach, (iv) variable importance, and (v) managing computing resources. We will demonstrate the techniques by building models to predict device failures from connected monitors (low dimensional) and to predict end of life events from medical records and claims data (high dimensional).

29/01/2021 às  13:30 hs - Local: Canal do Youtube: Video conferencia do DEST 

Fabrizio Ruggeri (CNR IMATI,Italy)

Título: Likelihood-Free Parameter Estimation for Dynamic Queueing Networks The case of the immigration queue at an international airport.


Many complex real-world systems such as airport terminals, manufacturing processes and hospitals are modelled with networks of queues. To estimate parameters, restrictive assumptions are usually placed on these models. For instance arrival and service distributions are assumed to be time-invariant. Violating this assumption are so-called dynamic queueing networks (DQNs) which are more realistic but do not allow for likelihood-based parameter estimation. We consider the problem of using data to estimate the parameters of a DQN. The is the first example of Approximate Bayesian Computation (ABC) being used for parameter inference of DQNs. We combine computationally efficient simulation of DQNs with ABC and an estimator for maximum mean discrepancy. DQNs are simulated in a computationally efficient manner with Queue Departure Computation (a simulation techniques we are proposing), without the need for time-invariance assumptions, and parameters are inferred from data without strict data-collection requirements. Forecasts are made which account for parameter uncertainty. We embed this queueing simulation within an ABC sampler to estimate parameters for DQNs in a straightforward manner. We motivate and demonstrate this work with the example of passengers arriving at the passport control in an international airport.

Joint work with Anthony Ebert, Kerrie Mengersen, Paul Wu, Antonietta Mira and Ritabrata Dutta. Available:

22/01/2021 às  13:30 hs - Local: Canal do Youtube: Video conferencia do DEST 

Francisco Cribari-Neto (Departamento de Estatística-UFPE)

Título: Improved testing inferences for beta regressions with parametric mean link function.


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.

15/01/2021 às 14:30 hs - Local: Canal do Youtube: Video conferencia do DEST 

Manuel Galea (Pontificia Universidad Catolica de Chile)

Título: Robust inference in the Capital Assets Pricing Model using the multivariate t−distribution.


In this work we consider the Capital Asset Pricing Model under the multivariate t−distribution with finite second moment. This distribution, which contain the normal distribution, offer a more flexible framework for modeling asset returns. The main objective is to develop statistical inference tools, such as parameter estimation and linear hypothesis tests in asset pricing models, with an emphasis on the Capital Asset Pricing Model (CAPM). An extension of the CAPM, the Multifactor Asset Pricing Model (MAPM), is also discussed. A simple algorithm to estimate the model parameters, including the kurtosis parameter, is implemented. Analytical expressions for the Score function and Fisher information matrix are provided. For linear hypothesis tests, the four most widely used tests (likelihood-ratio, Wald, score, and gradient statistics) are considered. In order to test the mean-variance efficiency, explicit expressions for these four statistical tests are also presented. The results are illustrated using two real data sets: the Chilean Stock Market data set and another from the New York Stock Exchange. The asset pricing model under the multivariate t-distribution presents a good fit, clearly better than the asset pricing model under the assumption of normality, in both data sets.


08/01/2021 às 13:30 hs - Local: Canal do Youtube: Video conferencia do DEST 

Ivair Silva (UFOP)

Título: Fixed-Length Confidence Intervals Following a Sequential Test with Binomial Data.


Sample size and time to detect a signal are key performance measures in statistical sequential hypothesis testing. While the former should be optimized in Phase III clinical trials, minimizing the latter is of major importance in post-marked drug and vaccine safety surveillance of adverse events. However, in practice, even when strong evidences indicate that the surveillance could be stopped for drawing a test decision, it may be necessary to continue collecting data in order to improve the precision of the point estimator. For binomial data, this paper presents a linear programming framework to find the optimal alpha spending that provides fixed-width and fixed-accuracy confidence intervals for the relative risk parameter. The solution permits minimization of expected time to signal, or expected sample size as needed. In addition, the method is extended for group sequential testing with variable Bernoulli probabilities. To illustrate, we use simulated data mimicking actual clinical trials on experimental COVID-19 treatments.Fixed-Length Confidence Intervals Following a Sequential Test with Binomial Data.


11/12/2020 às 13:30 hs - Local: Canal do Youtube: Video conferencia do DEST 

Marcos Prates (DEST-UFMG)

Título: Spatial Confounding Beyond Generalized Linear Mixed Models: Extension to Shared Components and Spatial Frailty Models.


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 "RASCO: An R package to Alleviate Spatial Confounding" 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..

04/12/2020 às 13:30 hs - Local: Canal do Youtube: Video conferencia do DEST 

Guido Moreira (Pós-Doc, DEST-UFMG)

Título: Analysis of presence-only data via exact Bayes, with model and effects identification.


This paper provides an exact modeling approach for the analysis of presence-only ecological data. The approach is also based on frequently used Inhomogeneous Poisson Process but unlike other approaches does not rely on model approximations for performing inference. Exactness is achieved via a data augmentation scheme. One of the augmented processes can be interpreted as the unobserved occurrences of the relevant species and its posterior distribution can be used to make predictions of the species over the region of study beyond the observer bias. The data augmentation also provides a natural Gibbs sampler to make Bayesian inference through MCMC. The proposal shows better AUC prediction metric than the traditional Poisson Process whose intensity function is log-linear with respect to the covariates, which is currently the standard method. Additionally, an identifiability problem that arises in the traditional model does not affect our proposal and this is verified on analyses with real ecological data.

06/11/2020 às 13:30 hs - Local: Canal do Youtube: Video conferencia do DEST 

Murray Pollock (Newcastle University, UK)

Título: The Restore Process - Practical CFTP by enriching Markov processes.


We develop a new class of Markov processes comprising local dynamics governed by a fixed Markov process, which are enriched with regenerations from a fixed distribution at a state-dependent rate. We give conditions under which such processes possess a given target distribution as their invariant measures, thus making them amenable for use within Monte Carlo methodologies. Enrichment imparts a number of desirable theoretical and methodological properties, which includes straightforward conditions for the process to be uniformly ergodic and possess a coupling from the past construction that enables exact sampling from the invariant distribution. Joint work with David Steinsaltz / Gareth Roberts / Andi Wang..

30/10/2020 às 10:00 hs - Local: Canal do Youtube: Video conferencia do DEST 

Leonardo Brandão (UFMG-Seminários 1B)

Título: The poly-logWeibull model applied to space-time interpolation of temperature.


In this paper, a multivariate log-Weibull model for spatially dependent data is defined by marginalizing a conditional Pareto distribution with respect to a shared spatial random effect of alpha-stable distributions. Some properties of this newmodel are derived, and procedures for the estimation and inference are discussed. An application is developed to study observed temperature data sets collected from weather stations in the Brazilian Amazon.

Paper by A. L. Mota, M. S. De Lima , F. N. Demarqui e L. H. Duczmal

23/10/2020 às 14:30 hs - Local: Canal do Youtube: Video conferencia do DEST 

Ramsés H. Mena (IIMAS-UNAM, Mexico)

Título: Beta-binomial stick-breaking non-parametric prior.


A new class of nonparametric prior distributions, termed Beta-Binomial stick-breaking process, is proposed. By allowing the underlying length random variables to be dependent through a Beta marginals Markov chain, an appealing discrete random probability measure arises. The chain’s dependence parameter controls the ordering of the stick-breaking weights, and thus tunes the model’s label-switching ability. Also, by tuning this parameter, the resulting class contains the Dirichlet process and the Geometric process priors as particular cases, which is of interest for MCMC implementations.

Some properties of the model are discussed and a density estimation algorithm is proposed and tested with simulated datasets.

Reference: Gil-Leyva, M.F., Mena, R.H. and Nicoleris, T. (2020). Beta-Binomial stick-breaking non-parametric prior Electronic Journal of Statistics. 14, 1479-1507.

16/10/2020 às 13:30 hs - Local: Canal do Youtube: Video conferencia do DEST 

Raffaele Argiento (Department of Statistics, Università Cattolica del Sacro Cuore)

Título: Is Infinity That Far? A Bayesian Nonparametric Perspective of Finite Mixture Models.


Mixture models are one of the most widely used statistical tools when dealing with data from heterogeneous populations. This talk considers the long-standing debate over finite mixture and infinite mixtures and brings the two modelling strategies together, by showing that a finite mixture is simply a realization of a point process. Following a Bayesian nonparametric perspective, we introduce a new class of prior: the Normalized Independent Point Processes. We investigate the probabilistic properties of this new class. Moreover, we design a conditional algorithm for finite mixture models with a random number of components overcoming the challenges associated with the Reversible Jump scheme and the recently proposed marginal algorithms. We illustrate our model on real data and discuss a relevant application in population genetics.

09/10/2020 às 14:00 hs - Local: Canal do Youtube: Video conferencia do DEST 

Peter Müller (University of Texas)

Título: Bayesian Categorical Matrix Factorization via Double Feature Allocation.


We propose a categorical matrix factorization method to infer latent diseases from electronic health records data. A latent disease is defined as an unknown cause that induces a set of common symptoms for a group of patients. The proposed approach is based on a novel double feature allocation model which simultaneously allocates features to the rows and the columns of a categorical matrix. Using a Bayesian approach, available prior information on known diseases greatly improves identifiability of latent diseases. This includes known diagnoses for patients and known association of diseases with symptoms. For application to large data sets, as they naturally arise in electronic health records, we develop a divide-and-conquer Monte Carlo algorithm, which allows inference for the proposed double feature allocation model, and a wide range of related Bayesian nonparametric mixture models and random subsets. We validate the proposed approach by simulation studies including mis-specified models and comparison with sparse latent factor models. In an application to Chinese electronic health records (EHR) data, we find results that agree with related clinical and medical knowledge.


1) Bayesian Double Feature Allocation for Phenotyping with Electronic Health Records, Yang Ni, Peter Mueller, Yuan Ji

Journal of the American Statistical Association, in press.


2) Consensus Monte Carlo for Random Subsets using Shared Anchors, Yang Ni, Yuan Ji, and Peter Mueller

Journal of Computational and Graphical Statistics}, in press.

02/10/2020 às 13:30 hs - Local: Canal do Youtube: Video conferencia do DEST 

Alexandre Galvão Patriota (USP)

Título: Modelos de regressão elípticos com parametrização geral.

Resumo: Neste seminário irei apresentar alguns dos resultados assintóticos desenvolvidos considerando modelos de regressão elípticos com parametrização geral. Estes modelos incluem modelos mistos, modelos não lineares heteroscedásticos, modelos com erros nas variáveis, entre outros.

25/09/2020 às 13:30 hs - Local: Canal do Youtube: Video conferencia do DEST 

Kelly Cristina Mota Gonçalves (DME-UFRJ)

Título: Bayesian dynamic quantile linear models and some extensions.

Resumo: The main aim of this talk is to present a new class of models, named dynamic quantile linear models. It combines dynamic linear models with distribution free quantile regression producing a robust statistical method. This class of models provides richer information on the effects of the predictors than does the traditional mean regression and it is very insensitive to heteroscedasticity and outliers, accommodating the non-normal errors often encountered in practicalapplications. Bayesian inference for quantile regression proceeds by forming the likelihood function based on the asymmetric Laplace distribution and a location-scale mixture representation of it allows finding analytical expressions for the conditional posterior densities of the model. Thus, Bayesian inference for dynamic quantile linear models can be performed using an efficient Markov chain Monte Carlo algorithm or a fast sequential procedure suited for high-dimensional predictive modeling applications with massive data. Finally, a hierarchical extension, useful to account for structural features in the dataset, will be also presented.

18/09/2020 às 13:30 hs - Local: Canal do Youtube: Video conferencia do DEST 

Denise Duarte (DEST-UFMG)

Título: Modelos de redes de afinidade

Resumo: Uma das abordagens mais populares atualmente na literatura sobre dados relacionais é a Análise de Redes Complexas. Consequentemente, análises estatísticas sobre redes sociais buscaram acompanhar este crescimento para atender à esta demanda. Para modelar estatisticamente os fenômenos estudados em redes socais, modelos probabilísticos em grafos aleatórios tem sido bastante utilizados. Entretanto, as redes sociais possuem características que são diferentes dos modelos de grafos aleatórios que possuem arestas independentes. A proposta deste trabalho é apresentar e estudar um modelo de grafo aleatório onde as ligações (arestas) são baseadas nas características dos vértices, permitindo uma modelagem mais realista de uma rede. Propomos uma vasta família de modelos, que chamamos de Modelos de Redes de Afinidade, onde as conexões são valoradas segundo uma função que mensura a afinidade entre os atores da rede. Além disso, as conexões são realizadas a partir de um determinado ponto de corte para o valor desta função afinidade, de acordo com o nível de afinidade desejado pelo pesquisador. Para exemplificar o estudo do comportamento do nosso modelo, elaboramos um estudo simulado baseado em simulações de Monte Carlo para uma das funções de afinidade descritas neste trabalho. Realizamos uma calibração nos parâmetros geradores do modelo, analisando suas medidas topológicas, comparando com as medidas topológicas encontradas em grafos com a mesma distribuição de afinidade, mas com arestas sorteadas independentemente. O estudo mostra que o Modelo de Redes de Afinidade consegue capturar características importantes de redes sociais. Trabalho em conjunto com Wesley H.S. Pereira e Rodrigo B. Ribeiro.

11/09/2020 às 13:30 hs - Local: Canal do Youtube: Video conferencia do DEST 

Flávio Bambirra Gonçalves (DEST-UFMG)

Título: Exact and computationally efficient Bayesian inference for generalized Markov modulated Poisson processes

Resumo: Statistical modeling of point patterns is an important and common problem in several areas. The Poisson process is the most common process used for this purpose, in particular, its generalization that considers the intensity function to be stochastic. This is called a Cox process and different choices to model the dynamics of the intensity gives raise to a wide range of models. We present a new class of unidimensional Cox process models in which the intensity function assumes parametric functional forms that switch among them according to a continuous-time Markov chain. A novel methodology is proposed to perform exact Bayesian inference based on MCMC algorithms. The term exact refers to the fact that no discrete time approximation is used and Monte Carlo error is the only source of inaccuracy. The reliability of the algorithms depends on a variety of specifications which are carefully addressed, resulting in a computationally efficient (in terms of computing time) algorithm and enabling its use with large datasets. Simulated and real examples are presented to illustrate the efficiency and applicability of the proposed methodology. A specific model to fit epidemic curves is proposed and used to analyze data from Dengue Fever in Brazil and COVID-19 in some countries.This is joint work with Livia Dutra and Roger Silva.

04/09/2020 às 13:00 hs - Local: Canal do Youtube: Video conferencia do DEST 

Hedibert Freitas Lopes (Insper-SP)

Título: The Illusion of the Illusion of Sparsity

Resumo: The emergence of Big Data raises the question of how to model statistical series when there is a large number of possible regressors. This article addresses the issue by comparing the possibility of using dense or sparse models in a Bayesian approach, allowing for variable selection and shrinkage. We discuss the results reached by Giannone, Lenza, and Primiceri (2018) through a “Spike-and-Slab” prior, which suggest an “illusion of sparsity” in economic datasets, as no clear patterns of sparsity could be found. We make a further revision of the posterior distributions of the model, and propose three experiments to evaluate the robustness of the adopted prior distribution. We find that the model indirectly induces variable selection and shrinkage, what suggests that the “illusion of sparsity” is, itself, an illusion. Note: Joint work with Bruno Vinicius Nunes Fava and was part of his 2019 undergraduate final projection Economics at Insper. Bruno starts his PhD in Economics at Northwestern University in August 2020.

28/08/2020 às 11:00 hs - Local: Canal do Youtube: Video conferencia do DEST 

Oliver Stone and Theo Economou (Institute for Data Science and Artificial IntelligenceUniversity of Exeter)

Título: Bayesian Hierarchical Frameworks for Correcting Under-reporting and Delayed Reporting of Count Data

Resumo: The Covid-19 pandemic has brought renewed attention on the limitations of systems which report cases and deaths, specifically under-reporting and delayed reporting. In this two-part seminar, we will discuss Bayesian hierarchical approaches to correcting these issues, to enable enhanced monitoring and decision-making. Finally, we will demonstrate how the framework for correcting delayed reporting can be used for now-casting and forecasting of English hospital deaths from Covid-19.


21/08/2020 às 13:30hs - Local: Canal do Youtube: Video conferencia do DEST

Rafael Bassi Stern (UFSCar)

Título: CD-Split: Efficient Conformal Regions in High Dimensions

14/08/2020 às 13:30h - Local: Canal do Youtube: Video conferencia do DEST

Luiz Max Carvalho (EMAP-FGV)

Título: Adaptive Markov chain Monte Carlo on the space of time-calibrated trees

20/03/2020 às 13:30h - Local: sala 2076 - ICEx 

Luiz Max Carvalho (EMAP-FGV)

Título: Efficient transition kernels for Bayesian phylogenetics

13/03/2020 às 13:30h - Local: sala 2076 - ICEx 

Vinicius Mayrink (DEST-UFMG)

Título: Structural equation modeling with time dependence: an application comparing Brazilian energy distributors


06/12/2019 às 14:30h - Local: sala 2076 - ICEx 

Walmir dos Reis Miranda Filho (DEST-UFMG)

Título: Frailty and Copula Models: Similarities and Differences

04/12/2019 às 14:30h - Local: sala 2076 - ICEx 

Daiane Zuanetti (UFSCar)

Título: Subset nonparametric Bayesian clustering - an application in genetic data

04/12/2019 às 13:30h - Local: sala 2076 - ICEx 

Rafael Izbicki (UFSCar)

Título: Quantification under prior probability shift: the ratio estimator and its extensions

29/11/2019 às 13:30h - Local: sala 2076 - ICEx 

Edson Ferreira (DEST)

Título: Context Tree Estimation for Not Necessarily Finite Memory Processes, Via BIC and MDL

22/11/2019 às 14:30h - Local: sala 2076 - ICEx 

Renan Xavier Cortes (Anglo American) 

Título: Building open-source tools in Python for Spatio-Temporal Data and Modelling

22/11/2019 às 13:30h - Local: sala 2076 - ICEx 

Patricia Viana (DEST-UFMG)

Título: Bayesian Cluster Analysis: Point Estimation and Credible Balls

08/11/2019 às 13:30h - Local: sala 2076 - ICEx 

Jussiane Gonçalves (DEST-UFMG)

Título: Zero-inflated mixed Poisson regression models

01/11/2019 às 14:30h - Local: sala 2076 - ICEx 

Diogo Carlos dos Santos (UFMG)

Título: O processo de percolação de grau restrito

01/11/2019 às 13:30h - Local: sala 2076 - ICEx 

Guilherme Ludwig (UNICAMP)

Título: Interacting cluster point process model for epidermal nerve fiberss

25/10/2019 às 13:30h - Local: sala 2076 - ICEx 

Glaura C. Franco (DEST-UFMG)

Título: Non-Gaussian Time Series Models

18/10/2019 às 13:30h -  Local: sala 2076 - ICEx 

Ronald Dickman (Física-UFMG)

Título: Steady-state thermodynamics and phase coexistence far from equilibrium

11/10/2019 às 13:30h -  Local: sala 2076 - ICEx 

Dani Gamerman (DEST e UFRJ))

Título: Modelagem hierárquica em problemas de alta dimensão.

04/10/2019 às 13:30h -  Local: sala 2076 - ICEx 

Fabricio Murai (DCC-UFMG)

Título: Reasoning from Partially Observed Networks: Sampling, Estimation and Models.

27/09/2019 às 13:30h -  Local: sala 2076 - ICEx 

Ilka Afonso Reis (DEST/UFMG)

Título: Um breve passeio pela Psicometria: minha experiência com validação de instrumentos.

20/09/2019 às 13:00h -  Local: sala 2076 - ICEx 

Vera Tomazella (UFSCar)

Título: Defective Models Induced By Gamma Frailty Term for Survival Data With Cured Fraction

13/09/2019 às 13:30h -  Local: sala 2076 - ICEx 

Roberto Nalon (DCC- Big Data)

Título: Detecting Spatial Clusters of Disease Infection Risk Using Sparsely Sampled Social Media Mobility Patterns

11/09/2019 às 11:00h -  Local: LCC - ICEx 

Ian M Danilevicz (DEST)

Título: An overview of robust spectral estimators

06/09/2019 às 13:30h -  Local: Auditório III do - ICEx 


Na sexta-feira dia 06 de setembro, o Departamento de Estatística estará recebendo, no Auditório III do ICEx, o Data Scientist, Marcus Watari, e o Data Engineer, Daniel Golhiardi, ambos consultores da McKinsey & Company. Eles apresentarão um Seminário para alunos da Pós-graduação em Estatística, Química, Física, Ciência da Computação, Matemática e Engenharia Elétrica. Mostrarão casos de como a ciência de dados tem sido aplicada em contextos reais em diferentes indústrias e quais são os desafios e possibilidades de atuação na carreira de um Engenheiro e Cientista de Dados.


 23/08/2019 às 13:30h -  Local: sala 2076 - ICEx 

Marcelo Azevedo Costa (Eng. Produção – UFMG)

Título: Failure detection in robotic arms using statistical modeling, machine learning and hybrid gradient boosting

09/08/2019 às 13:30h -  Local: sala 2076 - ICEx 

Michel Spira (Departamento de Matemática - UFMG)

Título: Matemática e o Homem Vitruviano

01/08/2019 às 14:00h -  Local: sala 3060 - ICEx 

Alexandre Gaudillière (CNRS-Marseille); Joint work: A. Bianchi (Universita di Padova); P. Milanesi (Universite d'Aix-Marseille); M. E. Vares(UFRJ)

Título: Exponential transition law for the kinetic Ising model. 


05/07/2019 às 14:30h -  Local: sala 2076 - ICEx 

Fernanda Gabriely Batista Mendes (DEST)

SEMINÁRIO 2 - Título: Construção de cadeia de Markov estacionária. 

05/07/2019 às 13:30h -  Local: sala 2076 - ICEx 

Adrian Luna (DEST) 

SEMINÁRIO 1 - Título: Redes Aleatórias: alguns desafios. 

28/06/2019 às 15:00h -  Local: sala 2076 - ICEx 

Hernando Ombao - Biostatistics Research Group - STAT Program - King Abdullah University of Science and Technology (KAUST, Saudi Arabia)

Título: Exploring the Dependence Structure in Multivariate Time Series.

28/06/2019 às 14:00h -  Local: sala 2076 - ICEx 

Hernando Ombao - Biostatistics Research Group - STAT Program - King Abdullah University of Science and Technology (KAUST, Saudi Arabia)

Título: Spectral and Coherence Analysis: Basic Ideas and Applications.

07/06/2019 às 13:30h -  Local: sala 2076 - ICEx 

Michelle Miranda (University of Victoria no Canada)

Título: Modeling Modern Data Objects: Statistical Methods for Ultra-high Dimensionality and Intricate Correlation Structures.

31/05/2019 às 13:30h -  Local: Auditório B 106 - CAD3 

Magda Carvalho Pires (DEST-UFMG)

Título: Current status data com censura informativa e erros de classificação

Este seminário é integra a programação do V Encontro Comemorativo do Dia do Estatístico. Por favor inscreva-se: (

24/05/2019 às 13:30h - CHICO Soares (Prof. Emérito da UFMG)

Título: Minhas Estatísticas

17/05/2019 às 13:30h - Marcos O. Prates (EST-UFMG)

Título: Assessing spatial confounding in Bayesian shared component disease mapping models via SPOCK: With applications to SEER cancer data

03/05/2019 às 13:30h - Afrânio M C Vieira (UFSCAR)

Título: Modelos de Resposta ao Item modificados para fontes de heterogeneidade conhecidas e desconhecidas.

26/04/2019 às 13:30h - Fábio Nogueira Demarqui (DEST-UFMG)

Título: An unified semiparametric approach to model survival data with crossing survival curves

12/04/2019 às 13:30h - Guilherme Augusto Veloso (PG-EST)

Título: Análise Bayesiana Sequencial de Dados Multivariados de Contagem

29/03/2019 às 14:00h - Frederico R. B. Cruz (DEST-UFMG)

Título: Estimação e Otimização em Filas e Aplicações

29/03/2019 às 13:00h - Euloge Clovis Kenne Pagui (Università di Padova, Itália)

Título: Bias reducing adjusted score functions for monotone likelihood in Cox Regression

21/03/2019 às 14:00h - Nitis Mukhopadhyay (Department of Statistics - University of Connecticut)

Título: On Asymptotic Normality of Standardized Stopping Times with Illustrations


07/12/2018 às 13:30h - Douglas Mateus da Silva

Título: Estimador subsemble espacial para dados massivos em geoestatística.

30/11/2018 às 13:30h - Juliana Vilela Bastos (Coordenadora do Programa Traumatismos Dentários da Faculdade de Odontologia da UFMG)

Título: Metodologia e Estatística na Pesquisa em Traumatismos Dentários

30/11/2018 às 14:30h - Profa. Jussiane Gonçalves (UFMG)

Título: Modelagem de sobredispersão tempo-dependente em dados de contagem longitudinal

23/11/2018 às 10:00h - Prof. Murray Pollock (Un. of Warwick)

Título: Modelo de regressão de Cox com verossimilhança monótona

23/11/2018 às 13:30h - Frederico Machado Almeida

Título: Confusion: Developing an information-theoretic secure approach for multiple parties to pool and unify statistical data, distributions and inferences.

23/11/2018 às 14:30h - Luis Alejandro Másmela Caita

Título: Imputação Múltipla para dados ausentes de maneira não-aleatória

09/11/2018 às 13:30h - Arthur Tarso Rego

Título: Abordagem via Modelos de Espaço de Estados para Séries Temporais Financeiras

26/10/2018 às 14:30h - Guilherme Aguilar

Título: Bayesian linear regression models with flexible error distributions

26/10/2018 às 13:30h - Danna L. Cruz

Título: Spatial disease mapping using Directed Acyclic Graph Auto-Regressive (DAGAR) models

19/10/2018 às 14:30h - Profa. Thais C. O. Fonseca (DME-UFRJ)

Título: Reference Bayesian analysis for hierarchical models

19/10/2018 às 13:30h - Prof. Karthik Bharath (University of Nottingham, UK)

Título: Geometric statistical methods for imaging data

28/09/2018 às 13:30h - Larissa Sayuri Futino C. dos Santos (UFMG)

Título: Ampliando Horizontes: Vendo o mundo com outros olhos

14/09/2018 às 13:30h - Prof. Tohid Ardeshiri (Linköping University, Suécia)

Título: Analytical Approximations for Bayesian Inference

31/08/2018 às 14:30h - Josemar Rodrigues (UFSCar)

Título: Bayesian superposition of pure-birth destructive cure processes for tumor latency

3108/2018 às 13:30h - Reinaldo B. Arellano-Valle (Pontícia Universidad Católica de Chile)

Título: Scale and Shape Mixtures of Multivariate Skew-Normal Distributions

24/08/2018 às 13:30h - Roger W. C. da Silva (DEST)

Título: Dimensional Crossover in Anisotropic Percolation on Z^{d+s}

17/08/2018 (sexta-feira) às 13:30h - Ali Abolhassani (Department of Mathematical Sciences, Isfahan University of Technology, Isfahan, Iran)

Título: Bell Spatial Scan Statistics

08/08/2018 (quarta-feira) às 10:30h - Silvia L. P. Ferrari (USP)

Título: Box-Cox t random intercept model for estimating usual nutrient intake distributions


22/06/2018 às 14:30h - Túlio Lima (Departamento de Estatística - UFMG)

Título: Comparison between risk measures and ruin probability for the calculation of solvency capital for a long-term guarantee.

18/05/2018 às 13:30h - Rodrigo Bernardo da Silva (Departamento de Estatística, UFPb)

Título: Flexible and Robust Mixed Poisson INGARCH Models.

11/05/2018 às 13:30h - Vinicius D. Mayrink (Departamento de Estatística, UFMG)

Título: Estendendo o JAGS: Distribuição exponencial por partes e geoestatística.

04/05/2018 às 13:30h - Caio L. N. Azevedo - Departamento de Estatística, IMECC, Unicamp

Título: Time series and multilevel modeling for longitudinal item response theory data

27/04/2018 às 13:30h - Valdério A. Reisen - UFES

Título: An overview of robust spectral estimators and its applications.

20/04/2018 às 13:30h - Pedro O. S. Vaz de Melo

Título: Futebol e Política não se discutem, se analisam! 

13/04/2018 às 13:30h - Carolina Silva Pena - Pró-Reitoria de Graduação - UFMG

Título: A new item response theory model to adjust data allowing examinee choice


01/12/2017 às 13:30h - Milton Pifano (DEST)

Título: Data clustering using generalized spatio-temporal dynamic factor analysis with interactions.

24/11/2017 às 13:30h - Guilherme L. de Oliveira (DEST)

Título: Modelos Partição Produto Espaciais.

24/11/2017 às 14:30h - Gabriela Oliveira (DEST)

Título: Aspectos Probabilísticos da Distribuição Laplace.

17/11/2017 às 13:30h - Alexandre Gaudillière (Aix Marseille Université, CNRS)

Título: Intertwining Wavelets.

17/11/2017 às 14:30h - Douglas Mesquita (DEST)

Título: Confundimento espacial em modelos de fragilidade.

10/11/2017 às 13:30h - Erick Amorim (DEST-UFMG)

Título: Agrupamentos através do processo Dirichlet e o modelo fatorial com interações

10/11/2017 às 14:30h - Rafael Alves (DEST-UFMG)

Título: Markov Graphs.

27/10/2017 às 13:30h - Juliana Freitas de Mello e Silva (DEST-UFMG)

Título: Modelagem conjunta de dados longitudinais e de sobrevivência.

20/10/2017 às 13:30h - Flávio Bambirra Gonçalves (DEST-UFMG)

Título: A Monte Carlo toolbox to solve intractable statistical problems: from retrospective sampling to Bernoulli Factories

29/09/2017 às 13:30h - Gilvan Ramalho Guedes (Depto. De Demografia-UFMG)

Título: Mudanças climáticas e economia: impactos sobre vulnerabilidade regional, oferta de trabalho e demanda por seguro

22/09/2017 às 13:30h - Fernando Quintana (PUC-Chile)

Título: Covariate-Dependent Mixture Models Induced by Determinantal Point Processes and Some Applications

15/09/2017 às 13:30h - Grupo Stats4Good (DEST-UFMG)

Título: Estatística para o Bem

01/09/2017 às 13:30h - Thais Paiva (DEST-UFMG)

Título: Imputation of multivariate continuous data with nonignorable missingness

25/08/2017 às 13:30h - Bernardo Nunes Borges de Lima (MAT-UFMG)

Título: A mágica sequência de de Bruijn

18/08/2017 às 11:10h - Sokol Ndreca (DEST)

Título: Asymptotics for the queueing system with exponentially delayed arrivals

16/08/2017 às 11:30h (excepcionalmente) - Iddo Ben-Ari (University of Connecticut - USA)

Título: Cut-off for a random walk with catastrophes


11/08/2017 às 14:30h - Ying Sun (King Abdullah University of Science and Technology (KAUST),Saudi Arabia)

Título: Visualization and Assessment of Spatio-temporal Covariance Properties


11/08/2017 às 13:30h - Marc G. Genton (King Abdullah University of Science and Technology (KAUST), Saudi Arabia)

Título: Directional Outlyingness for Multivariate Functional Data

07/07/2017 às 13:30h - Bárbara da Costa Campos Dias

Título: Exact Bayesian inference in spatio-temporal Cox processes driven by multivariate Gaussian processes

30/06/2017 às 13:30h - Uriel Moreira Silva

Título: Particle-based Inferente in Hidden Markov Models

23/06/2017 às 13:30h - Prof. Alexandre B. Simas (MAT-UFPb)

Título: Principal Components Analysis for Semimartingales and Stochastic PDE

09/06/2017 às 13:30h - Prof. Adrian P. H. Luna (DEST/UFMG)

Título: Misturas de Distribuições de Gibbs

02/06/2017 às 13:30h - Prof. Bernardo Lanza Queiroz (CEDEPLAR/UFMG)

Título: National and subnational experience with estimating the extent and trend in completeness of registration of deaths in Brazil and other developing countries

19/05/2017 às 13:15h (**Excepcionalmente**) - Prof. Fredy Castellares (DEST/UFMG)

Título:  Processo Múltiplo de Poisson e a Distribuição de Bell

28/04/2017 às 13:15h (**Excepcionalmente**) - Prof. Bernardo Nunes Borges de Lima (MAT/UFMG)

Título:  A mágica sequência de Bruijn

07/04/2017 às 13:30h - Prof. Marcos Oliveira Prates (DEST/UFMG)

Título: Um passeio por aplicações e problemas em diferentes áreas da Estatística nas quais tenho dedicado o meu tempo.

31/03/2017 às 13:30h - Profa. Denise Duarte (DEST/UFMG)

Título: Inferência para Cadeias de Markov de Alcance Variável Contaminadas Estocasticamente

24/03/2017 às 13:30h - Prof. Renato Martins Assunção (DCC/UFMG)

Título: De Fisher até o "Big Data": continuidades e descontinuidades