Improved shrinkage estimators in zero-inflated negative binomial regression model

Authors : Zahra ZANDİ, Hossein BEVRANİ‎, Reza ARABİ

Pages : 1855-1876

Doi:10.15672/hujms.911424

View : 28 | Download : 13

Publication Date : 2021-12-14

Article Type : Research Paper

Abstract :‎Zero-inflated negative binomial model is an appropriate choice to model count response variables with excessive zeros and over-dispersion simultaneously. ‎This paper addressed parameter estimation in the zero-inflated negative binomial model when there are many parameters, ‎so that some of them have not influence on the response variable. ‎We proposed parameter estimation based on the linear shrinkage, ‎pretest, ‎shrinkage pretest, ‎Stein-type, ‎and positive Stein-type estimators. ‎We obtained the asymptotic distributional biases and risks of the suggested estimators theoretically. ‎‎We also conducted a Monte Carlo simulation study ‎to compare the performance of each estimator with the unrestricted estimator using simulated relative efficiency insert ignore into journalissuearticles values(SRE); criterion.‎‎ ‎The results reveal that the SREs of proposed estimators are higher than the unrestricted estimator. ‎The suggested estimators were applied to the wildlife fish data to appraise their performance.
Keywords : Monte Carlo Simulation, Over dispersion, Shrinkage Estimators, ‎Zero inflated Negative Binomial Regression