Shrinkage estimators in inverse Gaussian regression model: Subject review.

Section: Review Paper
Issue
Vol. 19 No. 1 (2022): Volume 19 Issue 1
Published
Jun 25, 2025
Pages
46-53

Abstract

The presence of the high correlation among predictors in regression modeling has undesirable effects on the regression estimating. There are several available biased methods to overcome this issue. The inverse Gaussian regression model (IGRM) is a special model from the generalized linear models. The IGRM is a well-known model in research application when the response variable under the study is skewed data. Numerous biased estimators for overcoming the multicollinearity in IGRM have been proposed in the literature using different theories. An overview of recent biased methods for IGRM is provided. A comparison among these biased estimators allows us to gain an insight into their performance.

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Authors

Farah Abd ulghani

Department of Informatics & Statistic, College of Computer Science and Mathematical , University of Mosul, Mosul, Iraq

ORCID
Rafal Adeeb Othman

Department of Statistics and Informatics, College of Computer Sciences and Mathematics, University of Mosul, Mosul, Iraq

ORCID

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How to Cite

Abd ulghani, F., رفل, Adeeb Othman, R., & فرح. (2025). Shrinkage estimators in inverse Gaussian regression model: Subject review. IRAQI JOURNAL OF STATISTICAL SCIENCES, 19(1), 46–53. Retrieved from https://rjps.uomosul.edu.iq/index.php/stats/article/view/20997