A new biased estimator for the gamma regression model : Some applications in medical sciences

Jönköping University, IHH, Nationalekonomi, Finansiering och Statistik Department of Statistics, University of Sargodha, Sargodha, Pakistan Department of Statistics, University of Sargodha, Sargodha, Pakistan 2023

Akram, Muhammad N.
Amin, Muhammad
Qasim, Muhammad

The Gamma Regression Model (GRM) has a variety of applications in medical sciences and other disciplines. The results of the GRM may be misleading in the presence of multicollinearity. In this article, a new biased estimator called James-Stein estimator is proposed to reduce the impact of correlated regressors for the GRM. The mean squared error (MSE) properties of the proposed estimator are derived and compared with the existing estimators. We conducted a simulation study and employed the MSE and bias evaluation criterion to judge the proposed estimator’s performance. Finally, two medical dataset are considered to show the benefit of the proposed estimator over existing estimators.

Gamma regression model; James-Stein estimator; MSE; ridge regression; shrinkage estimator; Mean square error; Biased estimators; Evaluation criteria; James-Stein estimators; Mean squared error; Medical dataset; Multicollinearity; Regression model; Simulation studies; Regression analysis; Probability Theory and Statistics; Sannolikhetsteori och statistik; Article in journal; info:eu-repo/semantics/article; text

10.1080.03610926.2021.1977958

English

UPE oai:DiVA.org:hj-54761
0000-0003-0279-5305
doi:10.1080/03610926.2021.1977958
ISI:000697403600001
Scopus 2-s2.0-85115273501
Local ;intsam;54761
1293955668

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