# Penalized Variable Selection in Multi-Parameter Regression Survival Modelling

## TL;DR

This paper develops penalized variable selection methods for multi-parameter regression models in survival analysis, specifically applying them to Weibull MPR models, and evaluates their performance through simulations and real data.

## Contribution

It introduces penalized variable selection techniques tailored for multi-parameter regression models, extending existing methods to more complex survival models.

## Key findings

- Penalized methods effectively select variables in Weibull MPR models.
- Simulation studies demonstrate improved model performance.
- Real data application confirms practical utility.

## Abstract

Multi-parameter regression (MPR) modelling refers to the approach whereby covariates are allowed to enter the model through multiple distributional parameters simultaneously. This is in contrast to the standard approaches where covariates enter through a single parameter (e.g., a location parameter). Penalized variable selection has received a significant amount of attention in recent years: methods such as the least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD), and adaptive LASSO are used to simultaneously select variables and estimate their regression coefficients. Therefore, in this paper, we develop penalized multi-parameter regression methods and investigate their associated performance through simulation studies and real data; as an example, we consider the Weibull MPR model.