Search

Recent studies focus on modelling count data, which often shows overdispersion or underdispersion. The Conway–Maxwell–Poisson (COMP) regression model effectively handles such dispersion. However, multicollinearity can negatively impact the maximum likelihood estimator (MLE) by increasing variance. To address this, biased estimators like the ridge estimator have been suggested to mitigate multicollinearity effects. This research proposes a new COMP hybrid estimator to further address multicollinearity issues. Theoretical comparisons between the COMP hybrid estimator, MLE, and other COMP estimators reveal that the hybrid estimator reduces mean squared error. Monte Carlo simulations and practical applications confirm that these proposed estimators outperform both MLE and existing COMP estimators. [ABSTRACT FROM AUTHOR]

Mansson and Shukur (2011) investigated the performance of the Poisson ridge regression (PRR) estimator in terms of the mean square error (MSE) criterion. Similarly, Mansson (2012) investigated the performance of the Negative binomial ridge regression (NBRR) according to the MSE criterion. But there is no any analysis of the predictive performance of the PRR and NBRR estimators. Therefore, we define the PRR and the NBRR predictors to evaluate their predictive performances according to the prediction mean squared error under the target function. The Monte Carlo simulations and the real life numerical example are conducted to investigate the defined predictors' performance. [ABSTRACT FROM AUTHOR]

In this note, we make some comments about the paper of Alheety and Kibria (2014) and correct the wrongly proved Theorems in that paper. [ABSTRACT FROM AUTHOR]

This article discusses the predictive performance of the Liu type (LT) estimator compared to ordinary least squares, principal components, ridge regression, and Liu estimators. The theoretical results are illustrated by a numerical example and a region is established where the LT estimator is uniformly superior to the other mentioned estimators. [ABSTRACT FROM AUTHOR]

Multiple linear regression models are frequently used in predicting unknown values of the response variabley. In this case, a regression model's ability to produce an adequate prediction equation is of prime importance. This paper discusses the predictive performance of ther-kandr-dclass estimators compared to ordinary least squares (OLS), principal components, ridge regression and Liu estimators and between each other. The theoretical results are illustrated using Portland cement data and a region is established where ther-kand ther-dclass estimators are uniformly superior to the other mentioned estimators. [ABSTRACT FROM AUTHOR]