JOURNAL ARTICLE

Modified ridge-type estimator for the gamma regression model

Adewale F. LukmanKayode AyindeB. M. Golam KibriaEmmanuel T. Adewuyi

Year: 2020 Journal:   Communications in Statistics - Simulation and Computation Vol: 51 (9)Pages: 5009-5023   Publisher: Taylor & Francis

Abstract

The modified ridge-type estimator has been shown to cushion the effects of multicollinearity in the linear regression model. Recent studies have shown the adverse effects of multicollinearity in the gamma regression model (GRM). We proposed a gamma modified ridge-type estimator to tackle this problem. We derived the properties of this estimator and conducted a theoretical comparison with some of the existing estimators. A real-life example and simulation study show that the proposed estimator gains an advantage over other estimators in terms of the mean square error.

Keywords:
Multicollinearity Estimator Mean squared error Mathematics Ridge Statistics Minimum-variance unbiased estimator Regression analysis Bias of an estimator Regression Geology

Metrics

33
Cited By
5.66
FWCI (Field Weighted Citation Impact)
25
Refs
0.96
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Statistical Methods and Models
Physical Sciences →  Mathematics →  Statistics and Probability
Advanced Statistical Process Monitoring
Social Sciences →  Decision Sciences →  Statistics, Probability and Uncertainty
Statistical Methods and Inference
Physical Sciences →  Mathematics →  Statistics and Probability

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