Variational Full Bayes Lasso: Knots Selection in Regression Splines
Abstract
We develop a fully automatic Bayesian Lasso via variational inference. This is a scalable procedure for approximating the posterior distribution. Special attention is driven to the knot selection in regression spline. In order to carry through our proposal, a full automatic variational Bayesian Lasso, a Jefferey's prior is proposed for the hyperparameters and a decision theoretical approach is introduced to decide if a knot is selected or not. Extensive simulation studies were developed to ensure the effectiveness of the proposed algorithms. The performance of the algorithms were also tested in some real data sets, including data from the world pandemic Covid-19. Again, the algorithms showed a very good performance in capturing the data structure.
Cite
@article{arxiv.2102.13548,
title = {Variational Full Bayes Lasso: Knots Selection in Regression Splines},
author = {Larissa Alves and Ronaldo Dias and Helio S. Migon},
journal= {arXiv preprint arXiv:2102.13548},
year = {2021}
}
Comments
The authors contributed equally to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript