  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.
