ESTIMATION IN ZERO INFLATION POISSON REGRESSION MODEL WITH RIGHT CENSOR

Nguyễn Văn Trịnh; Phạm Văn Minh

doi:10.34238/tnu-jst.4636

ESTIMATION IN ZERO INFLATION POISSON REGRESSION MODEL WITH RIGHT CENSOR

About this article

Received: 11/06/21 Revised: 29/11/21 Published: 30/11/21

Authors

1. Nguyen Van Trinh , VietNam Maritime University
2. Pham Van Minh, VietNam Maritime University

Abstract

Count data often appears in many fields such as public health, economics, epidemiology... In order to handle this kind of data, some regression models have been developed as Poisson regression, Binomial regression or more generally are generalized linear regression models (GLMs). When count data contains extra of zeroes, zero-inflated (ZI) models are improved to suit. However, when counts are censored, the above models are no longer suitable. Therefore, Saffari and Adnan (2001) mentioned to this model using some simple simulations. However, the authors have not proven the existence, consistency, and asymptotic normality of a maximum likelihood estimator (MLE) yet. With that in mind, this paper develops theory to give out rigorous proof to handle the above problems basing upon the asymptotic normality theory.

Keywords

Excess of zeroes; Count data; MLE; Censor; Poisson model

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DOI: https://doi.org/10.34238/tnu-jst.4636

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