[ABE-L] Divulgação Seminário PPGEst UFRGS

[Fábio Mariano Bayer]

Prezados,

Temos o prazer de convidar a todos para o segundo seminário do Ciclo de
Seminários 2021 do Programa de Pós-Graduação em Estatística da UFRGS
(PPGEst-UFRGS). Informações abaixo:

*Evento*: Ciclo de Seminários 2021 do PPGEst-UFRGS

*Palestrante*: Dr. Marcos Oliveira Prates (UFMG)

*Título*: Spatial Confounding Beyond Generalized Linear Mixed Models:
Extension to Shared Components and Spatial Frailty Models

*Resumo*: Spatial confounding is defined as the confounding between the
fixed and spatial random effects in generalized linear mixed models
(GLMMs). It gained attention in the past years, as it may generate
unexpected results in modeling. We introduce solutions to alleviate the
spatial confounding beyond GLMMs for two families of statistical models. In
the shared component models, multiple count responses are recorded at each
spatial location, which may exhibit similar spatial patterns. Therefore,
the spatial effect terms may be shared between the outcomes in addition to
specifics spatial patterns. Our proposal relies on the use of modified
spatial structures for each shared component and specific effects. Spatial
frailty models can incorporate spatially structured effects and it is
common to observe more than one sample unit per area which means that the
support of fixed and spatial effects differs. Thus, we introduce a
projection-based approach for reducing the dimension of the data. An R
package named "RASCO: An R package to Alleviate Spatial Confounding" is
provided. Cases of lung and bronchus cancer in the state of California are
investigated under both methodologies and the results prove the efficiency
of the proposed methodology.

*Data*: 19 de julho de 2021 (segunda-feira)

*Horário*: 14h00 às 15:00 (50 min de apresentação e 10 min para perguntas)

*Local*: Ambiente virtual Mconf. Link: https://mconf.ufrgs.br/webconf/ppgest

*Moderadora*: Profa. Márcia Barbian (UFRGS)

Contamos com a presença de todos.

PPGEst-UFRGS

Fábio Bayer


"A big computer, a complex algorithm and a long time does not equal
science." Robert Gentleman
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