Variational Full Bayes Lasso: Knots Selection in Regression Splines

TL;DR

This paper introduces a fully automatic Bayesian Lasso method using variational inference, focusing on knot selection in regression splines, and demonstrates its effectiveness through simulations and real data applications including Covid-19 data.

Contribution

A novel variational Bayesian Lasso approach with automatic hyperparameter tuning and knot selection for regression splines.

Findings

Effective in capturing data structure in simulations

Performs well on real datasets including Covid-19 data

Scalable and automatic Bayesian spline modeling

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.