Improved Efficiency in Generalized Poisson Hurdle Model Estimation Using Restricted and Shrinkage Methods

Hayder Hasan Rahmah Al-Gharrawi, Hossein Bevrani and Basim Shlaibah Msallam

Journal of Mathematics, 2025, vol. 2025, 1-22

Abstract: This paper investigates the use of shrinkage estimators in the generalized Poisson hurdle (GPH) model for count data analysis. The GPH model effectively handles data with both excess zeros and over- or underdispersion. We propose shrinkage estimators to improve parameter estimation in this model and analyze their asymptotic properties, including biases and risks. An extensive comparison through Monte Carlo simulations evaluates the efficacy of the suggested estimators against the maximum likelihood estimator, employing a simulated relative efficiency criterion. In addition, we apply the estimators to two real-world datasets. Our findings illustrate that the suggested shrinkage estimators yield superior results compared to the traditional maximum likelihood estimator.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/jmath/2025/3104487.pdf (application/pdf)
http://downloads.hindawi.com/journals/jmath/2025/3104487.xml (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jjmath:3104487

DOI: 10.1155/jom/3104487

Access Statistics for this article

More articles in Journal of Mathematics from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().