[ABE-L] Seminários STODAD (Séries temporais, Ondaletas e Dados de Alta Dimensão)

[Airlane Pereira Alencar]

Dear colleagues,


Welcome to our online seminar that will be on Tuesday (April 19th) at 16:30
(Brasília time).

Link:
https://stream.meet.google.com/stream/6cf7fc13-d32e-44c4-a057-98d476a21ea6


*Bayesian Analysis and Variable Selection for Spatial Count Processes with
an Application to Rio de Janeiro Gun Violence Data*
Guilherme Ludwig (IMECC-UNICAMP), Yuan Wang (Washington State University),
Tingjin Chu (University of Melbourne), Haonan Wang (Colorado State
University), e Jun Zhu (University of Wisconsin-Madison).

Statistical analysis has been successfully applied to crime data for
identification of crime hot spots and prediction of future crimes. In this
paper, our main objective is to identify key factors for gun violence in
Rio de Janeiro and study the relationship between these key factors and the
number of reported events. We propose a double-layer stochastic Poisson
regression model for spatial count processes, which enables us to address
the over-dispersed count data and to handle the spatial correlation. A
Gibbs sampler is developed for sampling from the posterior distributions
with the help of augmentation of Pólya-Gamma auxiliary variables. We
further implement the nearest-neighbor Gaussian process model which scales
up the computation for large spatial data. Moreover, we propose a variable
selection method for key factor identification based on the spike-and-slab
prior distribution for the regression coefficients. Simulation studies are
used to demonstrate the performance of our proposed approach. Our analysis
of the gun violence data in Rio de Janeiro reveals the relationship between
violence events and socio-demographic covariates as well as an
interpretable spatial random effect that accounts for unmeasured covariate
information.


Keywords: Spatial statistics; Bayesian inference; Data augmentation;
Poisson regression.


Best regards.


STODAD team

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