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

• 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.

Ämnesord

NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)

NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)

Nyckelord

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

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