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22 11 im fatiado brazil
22 11 im fatiado england
22 11 im fatiado spain

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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Acompanhem a atualização da programação do nosso ciclo de palestras no sitio www.dme.ufrj.br, opção "Atividades", subopção "Ciclo de Palestras". Para ver palestras anteriores, inscreva-se no nosso canal Ciclo de Palestras Estatística UFRJ!

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

Confira AQUI o link para a transmissão.

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.

Título: A Prior for Record Linkage Based on Allelic Partitions

Palestrante: Brenda Betancourt (University of Florida)

Data: 23/09/20
Horário: 15h30

Resumo: In database management, record linkage aims to identify multiple records that correspond to the same individual. This task can be treated as a clustering problem, in which a latent entity is associated with one or more noisy database records. However, in contrast to traditional clustering applications, a large number of clusters with a few observations per cluster is expected in this context. In this paper, we introduce a new class of prior distributions based on allelic partitions that is specially suited for the small cluster setting of record linkage. Our approach makes it straightforward to introduce prior information about the cluster size distribution at different scales, and naturally enforces sublinear growth of the maximum cluster size – known as the microclustering property. We evaluate the performance of our proposed class of priors using three official statistics data sets and show that our models provide competitive results compared to state-of-the art microclustering models in the record linkage literature.

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.

Título: Approximate Bayesian Computation methods

Palestrante: Guilherme Souza Rodrigues (UnB)

Data: 09/09/2020
Horário: 15:30

A palestra ocorrerá remotamente, via Google Meets. Segue o link para o acesso a sala: meet.google.com/ruv-ruxx-ehg .

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

Resumo: Likelihood-free methods such as approximate Bayesian computation (ABC) have extended the reach of statistical inference to problems with computationally intractable likelihoods. Such approaches perform well for small-to-moderate dimensional problems, but suffer a curse of dimensionality in the number of model parameters. We will strive to provide a gentle overview of some of the state of the art approaches in this area.

Acompanhem a atualização da programação do nosso ciclo de palestras no sitio www.dme.ufrj.br opção Atividades subopção Ciclo de Palestras.

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