Shrinkage estimators in inverse Gaussian regression model: Subject review
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. Th...
| Main Authors: | , |
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| Format: | Article |
| Language: | Arabic |
| Published: |
College of Computer Science and Mathematics, University of Mosul
2022-06-01
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| Series: | المجلة العراقية للعلوم الاحصائية |
| Online Access: | https://stats.mosuljournals.com/article_174331_c98dec31afe34d44bba51e9e3a8d6608.pdf |
| Summary: | 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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| ISSN: | 1680-855X 2664-2956 |