Título: Bayesian estimation of the Average Treatment Effect for multilevel structured observations
Palestrante: Widemberg da Silva Nobre (DME/UFRJ)
Data:24/03/2021
Horário: 15:30h
Local: Transmissão online
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A sala será aberta sempre 10 minutos antes do início de cada sessão.
Resumo: We discuss Bayesian foundations for the estimation of the Average Treatment Effect (ATE) in the scenario of multilevel observations and in presence of confounding. Confounding occurs when a set of covariates (confounders) impact exposure and outcome simultaneously. In particular, we focus on scenarios when the set of confounders may include unobserved ones. We study the situation wherein multiple observations are made at a given location (e.g. individuals living across cities of a state). We explore the use of the propensity score approach through covariate adjustment to provide balancing of the treatment allocation (exposure). We discuss, through different simulation studies, the need to include location level random effects in the propensity score model to reduce bias in the estimation of the ATE. We also explore different prior specifications for the local level random effects. Our motivating example entails the effectiveness of the driven observed therapy (DOT) in the treatment of Tuberculosis (TB) for individuals who had TB across cities of the state of São Paulo, Brazil, in 2016. This is joint work with Alexandra M. Schmidt, Erica E. M. Moodie and David A. Stephens.
Título: Errors in election polls
Palestrante: Raphael Nishimura (University of Michigan)
Data:10/03/2021
Horário: 15:30h
Local: Transmissão online
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Resumo: Election polling poses a unique applied statistical challenge as it is one of the very few situations in which the finite population parameters are known at some point briefly after the estimation occurs. In recent years, election polls have been put into question given their performance in major elections, such as the 2016 U.S. Presidential Elections. Understanding what went wrong and finding solutions for their problems is of utmost importance for the survival of this important tool in democracies around the world. Statisticians are generally trained to account for sampling errors in surveys, but there are other sources of error that can have a larger impact on the quality of the survey estimates. The Total Survey Error (TSE) provides a good framework to understand these sources of error and how to address them through design or statistical adjustments. In this presentation, I will review the different sources of errors in election polls from a TSE perspective and present a few case-studies from past election polls to understand how these errors impacted their results.
Título: Radial Neural Networks
Palestrante: Carlos Tadeu Pagani Zanini (DME - UFRJ)
Data: 24/02/2021
Horario: 15:30h
Local: Transmissão online
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Resumo: This work proposes a very simple extension of the usual fully connected hidden layers in deep neural networks for classification. The objective is to transform the latent space on the hidden layers to be more suitable for the linear separation that occurs in the sigmoid/softmax output layer. We call such architectures radial neural networks because they use projections of fully connected hidden layers onto the surface of a hypersphere. We provide a geometrical motivation for the proposed method and show that it helps achieve convergence faster than the analogous architectures that they are built upon. As a result, we can significantly reduce training time on neural networks for classification that use fully connected hidden layers. The method is illustrated as an application to image classification, although it can be used for other classification tasks.
A sala será aberta sempre 10 minutos antes do início de cada sessão.
Título: Dynamical non-Gaussian modelling of spatial processes
Palestrante: Viviana das Graças Ribeiro Lobo (DME - UFRJ)
Data: 03/03/2021
Horario: 15:30h
Local: Transmissão online
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Resumo: Spatio-temporal processes in environmental applications are often assumed to follow Gaussian models, under or not particular transformations. However, heterogeneity in space and time may have patterns that are not accommodated by transforming the data in question. In such scenario, modelling the variance is paramount. The methodology presented in this paper adds flexibility to the usual Dynamical Gaussian model by defining the studied process as a scale mixture between a Gaussian process and Log-Gaussian one. The scale is represented by a process varying smoothly over space and time. State-space equations drive the dynamics over time for both response and variance processes resulting in a more computationally efficient estimation and prediction. Two applications are presented. The first one models the maximum temperature in the Spanish Basque Country and the following one models ozone levels in the UK dataset. They illustrate the effectiveness of our proposal in modelling varying variances over both time and space.Jointly with: Thais C. O. Fonseca (DME/UFRJ, Brazil) and Alexandra M. Schmidt (McGill University, Canada).
A sala será aberta sempre 10 minutos antes do início de cada sessão.
Título: A skewness and kurtosis comparison for continuous distributions
Palestrante: Fernanda De Bastiani (UFPE)
Data: 10/02/2021
Horario: 15:30h
Local: Transmissão online.
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Resumo: The main goal of this work is to compare the skewness and kurtosis of continuous distributions. It compares their moment skewness and kurtosis and compares their centile skewness and kurtosis. It shows the flexibility in skewness and kurtosis of different continuous distributions (within the gamlss R package), which is informative in the selection of an appropriate distribution. It introduces the bucket plot, a visual tool to detect skewness and kurtosis in a continuously distributed random variable. Join work with R. A. Rigby D. M. Stasinopoulos, G. Z. Heller and L. A. Silva.
A sala será aberta sempre 10 minutos antes do início de cada sessão.
