期刊
JOURNAL OF APPLIED STATISTICS
卷 49, 期 8, 页码 2016-2034出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/02664763.2021.1887103
关键词
Poisson regression; multicollinearity; ridge Poisson estimator; linearized ridge regression estimator; mean squared error
资金
- Shivaji University, Kolhapur, Maharashtra, INDIA [SU/C&U.D.Section/94/231]
Poisson regression is a commonly used technique for modeling count data, but maximum likelihood estimation can be unstable. A new estimator is proposed in this study to address multicollinearity issues, showing superior performance in terms of mean squared error.
Poisson regression is a very commonly used technique for modeling the count data in applied sciences, in which the model parameters are usually estimated by the maximum likelihood method. However, the presence of multicollinearity inflates the variance of maximum likelihood (ML) estimator and the estimated parameters give unstable results. In this article, a new linearized ridge Poisson estimator is introduced to deal with the problem of multicollinearity. Based on the asymptotic properties of ML estimator, the bias, covariance and mean squared error of the proposed estimator are obtained and the optimal choice of shrinkage parameter is derived. The performance of the existing estimators and proposed estimator is evaluated through Monte Carlo simulations and two real data applications. The results clearly reveal that the proposed estimator outperforms the existing estimators in the mean squared error sense.
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