Asymptotic properties of the maximum-likelihood estimator in zero-inflated binomial regression. Issue 20 (18th October 2017)

• Record Type: Journal Article
• Authors: Diallo, Alpha Oumar; Diop, Aliou; Dupuy, Jean-François
• Is Part Of: Communications in statistics. Volume 46:Issue 20(2017)
• Page Start: 9930
• Page End: 9948
• Publication Date: 2017-10-18
• Subjects: Asymptotic normality -- consistency -- count data -- excess of zeros -- simulations.; 62J12; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2016.1222437
• Languages: English
• ISSNs: 0361-0926

Abstract

ABSTRACT: The zero-inflated binomial (ZIB) regression model was proposed to account for excess zeros in binomial regression. Since then, the model has been applied in various fields, such as ecology and epidemiology. In these applications, maximum-likelihood estimation (MLE) is used to derive parameter estimates. However, theoretical properties of the MLE in ZIB regression have not yet been rigorously established. The current paper fills this gap and thus provides a rigorous basis for applying the model. Consistency and asymptotic normality of the MLE in ZIB regression are proved. A consistent estimator of the asymptotic variance–covariance matrix of the MLE is also provided. Finite-sample behavior of the estimator is assessed via simulations. Finally, an analysis of a data set in the field of health economics illustrates the paper.