# FATSO: A family of operators for variable selection in linear models

## TL;DR

This paper introduces FATSO, a new family of variable selection operators for linear models that are interpretable, based on LASSO geometry, and suitable for Bayesian inference, demonstrated with simulated and real data.

## Contribution

FATSO provides a novel family of variable selection operators that are interpretable and Bayesian-compatible, improving upon LASSO's limitations.

## Key findings

- Promising results on simulated data
- Effective variable selection in real data
- Interpretable tuning parameters

## Abstract

In linear models it is common to have situations where several regression coefficients are zero. In these situations a common tool to perform regression is a variable selection operator. One of the most common such operators is the LASSO operator, which promotes point estimates which are zero. The LASSO operator and similar approaches, however, give little in terms of easily interpretable parameters to determine the degree of variable selectivity. In this paper we propose a new family of selection operators which builds on the geometry of LASSO but which yield an easily interpretable way to tune selectivity. These operators correspond to Bayesian prior densities and hence are suitable for Bayesian inference. We present some examples using simulated and real data, with promising results.