11/12/2020 - Seminário - On the Effects of Spatial Confounding in Hierarchical Models - Palestrante: Alexandra M. Schmidt (McGill University - Canada)
Seminário Conjunto UFSCar/USP
Data e horário:
11/12/2020 às 14h
Local:
Google Meet - link: meet.google.com/iju-gnim-vue
Título: On the Effects of Spatial Confounding in Hierarchical Models
Palestrante:
Alexandra M. Schmidt (McGill University - Canada)
Resumo:
Usually, in spatial generalised linear models, covariates that are spatially smooth are collinear with spatial random effects. This affects the bias and precision of the regression coefficients. This is known in the spatial statistics literature as spatial confounding. We discuss the problem of confounding in the case of multilevel spatial models wherein there are multiple observations within clusters. We show that even under the standard multilevel model, which allows for independent (i.e. not spatially correlated) cluster effects, the cluster-level fixed effects might be biased depending on the structure of the ‘true’ generating mechanism of the processes. We provide simulation studies in order to investigate the effects of confounding in the estimation of fixed effects present in random intercept models under different scenarios of confounding. One remedy to spatial confounding is restricted spatial regression wherein the spatial random effects are constrained to be orthogonal to the fixed effects of the model. We propose one way to fit a restricted spatial regression model for multilevel data and illustrate it with artificial data analyses. We also investigate the effects of confounding in an analysis of student performance in the 2013 Brazilian Mathematical Olympics for Public Schools. This is joint work with Widemberg S. Nobre and João B. M. Pereira, from the Federal University of Rio de Janeiro, Brazil.