A New Ridge-Type Estimator for the Gamma Regression Model. (21st June 2021)

• Record Type: Journal Article
• Authors: Lukman, Adewale F.; Dawoud, Issam; Kibria, B. M. Golam; Algamal, Zakariya Y.; Aladeitan, Benedicta
• Other Names: Ayuga Francisco Academic Editor.
• Is Part Of: Scientifica. Volume 2021(2021)
• Publication Date: 2021-06-21
• Subjects: Life sciences -- Periodicals; Biology -- Periodicals; Medicine -- Periodicals; Biological Science Disciplines; Medicine; Biology; Life sciences; Medicine; Periodicals; Electronic journals; Periodicals; 500
• DOI: 10.1155/2021/5545356
• Languages: English
• ISSNs: 2090-908X

Abstract

Abstract : The known linear regression model (LRM) is used mostly for modelling the QSAR relationship between the response variable (biological activity) and one or more physiochemical or structural properties which serve as the explanatory variables mainly when the distribution of the response variable is normal. The gamma regression model is employed often for a skewed dependent variable. The parameters in both models are estimated using the maximum likelihood estimator (MLE). However, the MLE becomes unstable in the presence of multicollinearity for both models. In this study, we propose a new estimator and suggest some biasing parameters to estimate the regression parameter for the gamma regression model when there is multicollinearity. A simulation study and a real-life application were performed for evaluating the estimators' performance via the mean squared error criterion. The results from simulation and the real-life application revealed that the proposed gamma estimator produced lower MSE values than other considered estimators.