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Some ridge regression estimators for the zero-inflated Poisson model

Author

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  • B. Kibria
  • Kristofer Månsson
  • Ghazi Shukur

Abstract

The zero-inflated Poisson regression model is commonly used when analyzing economic data that come in the form of non-negative integers since it accounts for excess zeros and overdispersion of the dependent variable. However, a problem often encountered when analyzing economic data that has not been addressed for this model is multicollinearity. This paper proposes ridge regression (RR) estimators and some methods for estimating the ridge parameter k for a non-negative model. A simulation study has been conducted to compare the performance of the estimators. Both mean squared error and mean absolute error are considered as the performance criteria. The simulation study shows that some estimators are better than the commonly used maximum-likelihood estimator and some other RR estimators. Based on the simulation study and an empirical application, some useful estimators are recommended for practitioners.

Suggested Citation

  • B. Kibria & Kristofer Månsson & Ghazi Shukur, 2013. "Some ridge regression estimators for the zero-inflated Poisson model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(4), pages 721-735.
    • Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:721-735
    DOI: 10.1080/02664763.2012.752448
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    Cited by:

    1. Ruey-Ching Hwang & Chih-Kang Chu & Kaizhi Yu, 2021. "Predicting the Loss Given Default Distribution with the Zero-Inflated Censored Beta-Mixture Regression that Allows Probability Masses and Bimodality," Journal of Financial Services Research, Springer;Western Finance Association, vol. 59(3), pages 143-172, June.
    2. Zhi-Sheng Ye & Jian-Guo Li & Mengru Zhang, 2014. "Application of ridge regression and factor analysis in design and production of alloy wheels," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(7), pages 1436-1452, July.

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