Developing a ridge estimator for the gamma regression model

By Zakariya Yahya Algamal. Journal of Chemometrics, 2018

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The ridge regression model has been consistently demonstrated to be an attractive shrinkage method to reduce the effects of multicollinearity. The gamma regression model is a very popular model in the application when the response variable is positively skewed. However, it is known that multicollinearity negatively affects the variance of maximum likelihood estimator of the gamma regression coefficients. To address this problem, a gamma ridge regression model has been proposed. In this study, a new estimator is developed by proposing a modification of Jackknife estimator with gamma ridge regression model. Our Monte Carlo simulation results and the real data application suggest that the proposed estimator can bring significant improvement relative to other competitor estimators, in absolute bias and mean squared error.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8cd310827a54219cb607a0ed45f4e2e7b6f976a9