%D 2023
%T Regularized Models for Fitting Zero-Inflated and Zero-Truncated Count Data: A
Comparative Analysis
%K regularized models, ridge, lasso, zero
truncation, count data, health
%L eprints17575
%V 10
%P 1135-1141
%N 4
%J Mathematical Modelling of Engineering Problems
%A Olumide S Adesina
%A Dorcas M. Okewole
%A Adedayo F. Adedotun
%A Samuel K. Adekeye
%A S.O. Edeki
%A G. O. Akinlabi
%X Generalized Linear Models (GLMs) are widely recognized for their efficacy in fitting
count data, superior to the Ordinary Least Squares (OLS) approach. The incapability of
OLS to suitably handle count data can be attributed to its tendency to overfit. This study
proposes the utilization of regularized models, specifically Ridge Regression and the
Least Absolute Shrinkage and Selection Operator (LASSO), for fitting count data.
These models are compared to frequentist and Bayesian models commonly used for
count data fitting, such as the Dirichlet prior mixture of generalized linear mixed models
and the discrete Weibull. The findings reveal Ridge Regression's superiority over all
other models based on the Akaike Information Criterion (AIC). However, its
performance diminishes when evaluated using the Bayesian Information Criterion
(BIC), even though it still outperforms LASSO. The study thereby suggests the use of
regularized regression models for fitting zero-inflated count data, as demonstrated with
simulated data. Further, the appropriateness of regularized zero for zero-truncated count
is exemplified using life data.