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22 11 im fatiado brazil
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22 11 im fatiado spain

26 11 im noticia estatisticaTítulo: Bayesian time-varying quantile regression to extremes

Palestrante: Fernando Ferraz do Nascimento (UFPI)
Data: 02/12/2020
Horário: 15:30h
Local: Transmissão Online.

Confira AQUI o link para a transmissão.

Resumo: Maximum analysis consists of modeling the maximums of a data set by considering a specific distribution. Extreme value theory (EVT) shows that, for a sufficiently large block size, the maxima distribution is approximated by the generalized extreme value (GEV) distribution. Under EVT, it is important to observe the high quantiles of the distribution. In this sense, quantile regression techniques fit the data analysis of maxima by using the GEV distribution. In this context, this work presents the quantile regression extension for the GEV distribution. In addition, a time‐varying quantile regression model is presented, and the important properties of this approach are displayed. The parameter estimation of these new models is carried out under the Bayesian paradigm. The results of the temperature data and river quota application show the advantage of using this model, which allows us to estimate directly the quantiles as a function of the covariates. This shows which of them influences the occurrence of extreme temperature and the magnitude of this influence.

A sala será aberta sempre 10 minutos antes do início de cada sessão.


05 11 im noticia CicloPalestrasTítulo: Noncrossing structured additive multiple-output Bayesian quantile regression models

Palestrante: Bruno Ramos dos Santos (UFES)
Data: 11/11/20
Horário: 15h30
Local: Transmissão online

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Resumo: Quantile regression models are a powerful tool for studying different points of the conditional distribution of univariate response variables. Their multivariate counterpart extension though is not straightforward, starting with the definition of multivariate quantiles. In this talk, we show a flexible Bayesian quantile regression model when the response variable is multivariate,
where we are able to define a structured additive framework for all predictor variables. We build on previous ideas considering a directional approach to define the quantiles of a response variable with multiple outputs, and we define noncrossing quantiles in every directional quantile model. We define a Markov chain Monte Carlo (MCMC) procedure for model estimation,
where the noncrossing property is obtained considering a Gaussian process design to model the correlation between several quantile regression models. We illustrate the results of these models using two datasets: one on dimensions of inequality in the population, such as income and health; the second on scores of students in the Brazilian High School National Exam, considering three dimensions for the response variable.

Joint work with Thomas Kneib (University of Goettinge.

A sala será aberta sempre 10 minutos antes do início de cada sessão.


19 10 im noticia ciclodepalestrasTítulo: Approaches for combining data collected from multiple probability samples.

Palestrante: Marcel de Toledo Vieira (UFJF)
Data: 20/10/20
Horário: 16:00
Local: Transmissão online

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Resumo: Even though there is substantial literature on studies that pool survey data, it is still not clear which are the most efficient methodologies for pooling data from different surveys. For example, it is important to know whether the estimates from the different surveys involved should be given equal weights in the calculation of the combined statistics or not. If they are not given equal importance, then it should be clear how they should be weighted and why. In this paper, current and proposed methods considered to combine survey data are evaluated through simulation, in the context of simple random sampling, stratified random sampling and two stage cluster random sampling from finite populations generated from super-population models. Simulation results suggest superpopulation variance does not influence the choice of weighting method. However, the population size appear to influence this choice. Combining samples improved the precision of estimates regardless of the weighting method used for all sampling techniques.

*Joint work with Loveness Nyaradzo Dzikiti and Brendan Girdler-Brown from the University of Pretoria (South Africa)

A sala será aberta sempre 10 minutos antes do início de cada sessão.

23 10 im noticia ciclopalestrasppgTítulo: A Bayesian network-based approach for the Brazillian birth care system

Palestrante: Thais Cristina de Oliveira da Fonseca (DME - UFRJ)
Data: 27/10/20
Horário: 15:30h
Local: Transmissão online

Clique AQUI para assistir a transmissão. A sala será aberta sempre 10 minutos antes do início de cada sessão.

Resumo: This work investigates the causes of high rates (up to 88%) of the cesarean section (CS) in hospitals in Brazil. Evidence indicates that rates over 10-15% are correlated with maternal death, morbidity and near death. The usual approach to relate factors and outcome in the birth network is based on regression that do not allow for cause-effect inference. I propose a novel approach based on Bayesian networks to capture both non-linearities and complex cause-effect relations. The proposed network integrate both the knowledge from experts to elicit the graph structure and data of 12 hospitals (7200 women) to estimate model parameters. The theoretical birth network, even though described in papers in the area of public health, has not been mathematically constructed and confirmed by data. In particular, a quality improvement intervention called “Adequate Birth” (PPA) will be analyzed. The PPA was a pioneer project to reshape the birth care system in Brazil. The main results presented are (i) comprehensive guidelines to decrease CS rates depending on the estimated Bayesian network, (ii) integration of factors in a full model which will be tested using data obtained from the PPA intervention, (iii) query analysis based on changes in the system, (iv) a tool for policymakers aiming to optimize the cost-effectiveness of future interventions.

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05 10 im noticia ciclopalestras

Título: Statistical challenges in genotype by environment interactions and QTL by environment interactions

Palestrante: Paulo Canas Rodrigues (UFBA).
Data: 07/10/2020.
Horário: 15:30.
Local: Transmissão online.

Acesse AQUI o link para a transmissão online.

Resumo: When analyzing two-way data tables, with genotypes in the rows and environments in the columns, it is frequent to observe differential responses of genotypes across environments. This phenomenon is known as genotype by environment interaction (GEI) and can be defined by the change of genetic ranking of genotypes with the environment (e.g. in plant sciences, a genotype that is superior at well-watered conditions may yield poorly under dry conditions). The GEI can be expressed either as crossovers, when two different genotypes change in rank order of performance when evaluated in different environments, or inconsistent responses of some genotypes across environments without changes in rank order. One step further from the GEI can be made by considering the whole genetic information and analyze the QTL (quantitative trait loci) by environment interaction (QEI). To structure and better understand these interactions, the use of modern statistical methods is required. In this talk, I will present generalizations of two fixed effects models: the additive main effects and multiplicative interaction (AMMI) model, and the genotype plus genotype by environment interaction (GGE) model. These generalizations are the robust AMMI and robust GGE models, which outperform their classical counterparts when outlying observations are present in the data. I will present model performance and comparison in terms of QTL detection and QEI interpretation, by considering applications to simulated and real data sets.

A sala será aberta sempre 10 minutos antes do início de cada sessão.