The parameters in the Poisson regression model are usually estimated using the maximum
likelihood estimator (MLE). MLE suffers a breakdown when there is either multicollinearity or …

Recently, research papers have shown a strong interest in modeling count data. The over-
dispersion or under-dispersion are frequently seen in the count data. The count data …

Abstract The Conway–Maxwell Poisson (COMP) regression model is one of the count data
models to account for over–and under–dispersion. In regression analysis, when the …

The quasi-Poisson regression model is used for count data and is preferred over the
Poisson regression model in the case of over-dispersed count data. The quasi-likelihood …

The beta regression model is a flexible model, which widely used when the dependent
variable is in ratios and percentages in the range of (0.1). The coefficients of the beta …

The Poisson ridge estimator (PRE) is a commonly used parameter estimation method to
address multicollinearity in Poisson regression (PR). However, PRE shrinks the parameters …

Recent studies focus on modelling count data, which often shows overdispersion or
underdispersion. The Conway–Maxwell–Poisson (COMP) regression model effectively …

The negative binomial regression model is a widely adopted approach when dealing with
dependent variables that consist of non-negative integers or counts. This model serves as …

In this study, we developed a simple technique for effective parameter estimation and
prediction of the quantitative structure activity relationship studies using a two‐step …

The sensitivity of the least-squares estimation in a regression model is impacted by
multicollinearity and autocorrelation problems. To deal with the multicollinearity, Ridge, Liu …