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01 11 IM Palestras NoticiaTítulo: A strategy to impute age at onset of a particular condition from external sources

Palestrante: Danilo Alvares da Silva (PUC-Chile)
Data: 03/11/2021
Horário: 15:30h
Local: Transmissão online

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Resumo: A key hypothesis in epidemiological studies is that time to disease exposure provides relevant information to be considered in statistical models. However, the initiation time of a particular condition is usually unknown. Therefore, we developed a multiple imputation methodology for the age at onset of a particular condition, which is supported by incidence data from different sources of information. We introduced and illustrated such a methodology using simulated data in order to examine the performance of our proposal. Then, we analyzed the association of gallstones and fatty liver disease in the Maule Cohort, a Chilean study of chronic diseases, using participants’ risk factors and six sources of information for the imputation of the age-occurrence of gallstones. Simulated studies showed that an increase in the proportion of imputed data does not affect the quality of the estimated coefficients associated with fully observed variables, while the imputed variable slowly reduces its effect. For the Chilean study, the categorized exposure time to gallstones is a significant variable, in which participants who had short and long exposure have, respectively, 26.2% and 29.1% higher chance of getting a fatty liver disease than non-exposed ones. In conclusion, our multiple imputation approach proved to be quite robust both in the linear/logistic regression simulation studies and in the real application, showing the great potential of this methodology.

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A palestra ocorrerá remotamente, via plataforma Zoom (senha: 829338) e com transmissão ao vivo pelo canal do YouTube Ciclo de Palestras Estatística UFRJ.

A sala será aberta em torno 10 minutos antes do início de cada sessão. Acesse AQUI para mais informações no site do DME

02 03 im noticia palestrasppgTítulo: Reparameterized regression models

Palestrante: Marcelo Bourguignon Pereira (UFRN)
Data: 27/10/21
Horário: 15:30h
Local: Transmissão online

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senha: 829338

Resumo: Regression models are typically constructed to model the mean of a distribution. However, the density of several distributions is not indexed by the mean. In this context, this work provides a collection of regression models considering new parameterizations in terms of the mean and precision parameters. The main advantage of our new parametrizations is the straightforward interpretation of the regression coefficients in terms of the expectation, as usual in the context of generalized linear models.

A palestra ocorrerá remotamente, via plataforma Zoom e com transmissão ao vivo pelo canal do YouTube Ciclo de Palestras Estatística UFRJ.

A sala será aberta em torno 10 minutos antes do início de cada sessão. Acesse AQUI para mais informações.

04 10 IM Notícia EstatísticaTítulo: Decoupling Shrinkage and Selection in Gaussian Linear Factor Analysis

Palestrante: Hedibert Freitas Lopes (Insper)
Data: 06/10/2021
Horario: 15:30h
Local: Transmissão online

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Resumo: Factor Analysis is a popular method for modeling dependence in multivariate data. However, determining the number of factors and obtaining a sparse orientation of the loadings are still major challenges. In this paper, we propose a decision-theoretic approach that brings to light the relation between a sparse representation of the loadings and factor dimension. This relation is done through a summary from information contained in the multivariate posterior. To construct such summary, we introduce a three-step approach. In the first step, the model is fitted with a conservative factor dimension. In the second step, a series of point-estimates with a decreasing number of factors is obtained by minimizing an expected predictive loss function. In step three, the degradation in utility in relation to the sparse loadings and factor dimensions is displayed in the posterior summary. The findings are illustrated with a simulation study, and an application to personality data. We used different prior choices and factor dimensions to demonstrate the flexibility of the proposed method. This is joint work with
Henrique Bolfarine (USP), Carlos Carvalho (UT Austin) and Jared Murray (UT Austin).

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20 10 IM Populations NoticiaTítulo: Model-based Inference for Rare and Clustered Populations from Adaptive Cluster Sampling using Auxiliary Variables

Palestrante: Izabel Nolau (PPGE-UFRJ)
Data: 20/10/2021
Horario: 15:30h
Local: Transmissão online

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Resumo: Rare populations, such as endangered animals and plants, drug users and individuals with rare diseases, tend to cluster in regions. Adaptive cluster sampling is generally applied to obtain information from clustered and sparse populations since it increases survey effort in areas where the individuals of interest are observed. This work aims to propose a unit-level model which assumes that counts are related to auxiliary variables, improving the sampling process, assigning different weights to the cells, besides referring them spatially. The proposed model fits rare and grouped populations, disposed over a regular grid, in a Bayesian framework. The approach is compared to alternative methods using simulated data and a real experiment in which adaptive samples were drawn from an African Buffaloes population in a 24,108 square kilometers area of East Africa. Simulation studies show that the model is efficient under several settings, validating the methodology proposed in this paper for practical situations.

A palestra ocorrerá remotamente, via plataforma Zoom e com transmissão ao vivo pelo canal do YouTube Ciclo de Palestras Estatística UFRJ.

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

 

24 08 IM Noticia CicloDePalestrasTítulo: Random Machines: a new bagged-weighted support vector model

Palestrante :Anderson Luiz Ara Souza (UFBA)
Data: 25/08/2021
Horário: 15:30h
Local: Transmissão online

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Resumo: Improvement of statistical learning models to increase efficiency in solving classification or regression problems is a goal pursued by the scientific community. Particularly, the support vector machine model has become one of the most successful algorithms for this task. Despite the strong predictive capacity from the support vector approach, its performance relies on the selection of hyperparameters of the model, such as the kernel function that will be used. The traditional procedures to decide which kernel function will be used are computationally expensive. In this presentation, we proposed a novel framework to deal with the kernel function selection called Random Machines. The results display an improvement in the predictive capacity as well as reduced computational time.

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