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A new biased estimator for the gamma regression model : Some applications in medical sciences
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- Akram, Muhammad N. (author)
- Department of Statistics, University of Sargodha, Sargodha, Pakistan
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- Amin, Muhammad (author)
- Department of Statistics, University of Sargodha, Sargodha, Pakistan
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- Qasim, Muhammad (author)
- Jönköping University,IHH, Nationalekonomi, Finansiering och Statistik
- (creator_code:org_t)
- 2021-09-20
- 2023
- English.
- In: Communications in Statistics - Theory and Methods. - : Taylor & Francis. - 0361-0926 .- 1532-415X. ; 52:11, s. 3612-3632
- Journal article (peer-reviewed)
Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
Keyword
- 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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- ref (subject category)
- art (subject category)
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