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- Akram, Muhammad N.Department of Statistics, University of Sargodha, Sargodha, Pakistan (author)
A new biased estimator for the gamma regression model : Some applications in medical sciences
- Article/chapterEnglish2023
Publisher, publication year, extent ...
- 2021-09-20
- Taylor & Francis,2023
- printrdacarrier
Numbers
- LIBRIS-ID:oai:DiVA.org:hj-54761
- https://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-54761URI
- https://doi.org/10.1080/03610926.2021.1977958DOI
Supplementary language notes
- Language:English
- Summary in:English
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Notes
- 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 and genre
- NATURVETENSKAP Matematik Sannolikhetsteori och statistik hsv//swe
- NATURAL SCIENCES Mathematics Probability Theory and Statistics hsv//eng
- 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
Added entries (persons, corporate bodies, meetings, titles ...)
- Amin, MuhammadDepartment of Statistics, University of Sargodha, Sargodha, Pakistan (author)
- Qasim, MuhammadJönköping University,IHH, Nationalekonomi, Finansiering och Statistik(Swepub:hj)qasmuh (author)
- Department of Statistics, University of Sargodha, Sargodha, PakistanIHH, Nationalekonomi, Finansiering och Statistik (creator_code:org_t)
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- In:Communications in Statistics - Theory and Methods: Taylor & Francis52:11, s. 3612-36320361-09261532-415X
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