# Penalized Likelihood and Bayesian Function Selection in Regression Models

arXiv: https://arxiv.org/abs/1303.0670 · HTML: https://mlsanity.com/paper/1303.0670/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2013.

## Built on by (most cited first, 1 of 1)

- [Spike-and-Slab LASSO Generalized Additive Models and Scalable Algorithms for High-Dimensional Data Analysis](https://mlsanity.com/paper/2110.14449/) (2021)

## Abstract (arXiv)

Challenging research in various fields has driven a wide range of methodological advances in variable selection for regression models with high-dimensional predictors. In comparison, selection of nonlinear functions in models with additive predictors has been considered only more recently. Several competing suggestions have been developed at about the same time and often do not refer to each other. This article provides a state-of-the-art review on function selection, focusing on penalized likelihood and Bayesian concepts, relating various approaches to each other in a unified framework. In an empirical comparison, also including boosting, we evaluate several methods through applications to simulated and real data, thereby providing some guidance on their performance in practice.