Improved shrinkage estimators in zero-inflated negative binomial regression model

Yıl 2021, Cilt: 50 Sayı: 6, 1855 - 1876, 14.12.2021

Zahra Zandi Hossein Bevrani‎ Reza Arabi

https://doi.org/10.15672/hujms.911424

Cited By: 3

Öz

‎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 (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.

Anahtar Kelimeler

Monte Carlo Simulation, Over-dispersion, Shrinkage Estimators, ‎Zero-inflated Negative Binomial Regression

Kaynakça

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