The beta regression is a widely known statistical model when the response (or the
dependent) variable has the form of fractions or percentages. In most of the situations in beta …

The beta regression model is a commonly used when the response variable has the form of
fractions or percentages. The maximum likelihood (ML) estimator is used to estimate the …

ABSTRACT The Inverse Gaussian Regression Model (IGRM) is used when the response
variable is positively skewed and follows the inverse Gaussian distribution. In this article, we …

Multicollinearity in multinomial logistic regression affects negatively on the variance of the
maximum likelihood estimator. That leads to inflated confidence intervals and theoretically …

Rank-Based Methods for Shrinkage and Selection A practical and hands-on guide to the
theory and methodology of statistical estimation based on rank Robust statistics is an …

Abstract The Poisson Regression Model (PRM) is one of the benchmark models when
analyzing the count data. The Maximum Likelihood Estimator (MLE) is used to estimate the …

The Bell regression model (BRM), a member of the generalized linear models (GLMs), can
be used when the dependent variable consists of overdispersed count data. The maximum …

In logistic regression models, the maximum likelihood method is commonly used to estimate
the model parameters. However, unstable parameter estimates are obtained as a result of …

The Poisson Regression Model (PRM) is commonly used in applied sciences such as
economics and the social sciences when analyzing the count data. The maximum likelihood …

In linear regression model, ridge regression and two‐parameter Liu estimator (LE) are the
most widely used methods in recent decade to overcome the problem of multicollinearity …