Título: The variational inference Lasso: selecting knots in a regression splines
Palestrante: Larissa de Carvalho Alves (ENCE)
Local: Transmissão online.
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Resumo: Recent literature finds many alternative proposals for modeling and estimating a smooth function. In this talk, I focus on the variants of smoothing splines, called penalized regression splines. This is an attractive approach to modeling the nonlinear smoothing effects of covariates. This study discusses the knots selection and a penalty is introduced to control the selection of knots. The approach will be through a full Bayesian Lasso with variational inference. Choosing the appropriate number of knots and their position is a difficult problem, therefore we propose a two steps procedure. 1. For a fixed number of knots we use a full Bayesian Lasso, which combines features of shrinkage and variable selection, to obtain the relevant knots; 2. The number of knots is chosen based on the evidence lower bound (ELBO) over a grid of values.
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