December 2022 New zero-inflated regression models with a variant of censoring
Yasin Altinisik, Emel Cankaya
Author Affiliations +
Braz. J. Probab. Stat. 36(4): 641-674 (December 2022). DOI: 10.1214/22-BJPS544

Abstract

There is ever growing demand of modeling overdispersed count data generated by various disiplines. Excessive number of zeros and heterogeneity in the population are two main sources of the overdispersion problem. Development of new count models that are more flexible than conventional Poisson model is thus necessary in order to address such sources. This study fullfils this need by proposing a new heterogeneous Poisson model with a capture of excess zeros, namely zero-inflated Poisson–Ailamujia (ZIPA) model. In line with the aim of curing overdispersion, a censored variant of this newly suggested model is also here developed. An extensive simulation study is conducted to assess the performances of both forms of new models in terms of bias, precision and accuracy measures. Additionally, two real world applications are presented to illustrate practical implications of zero-inflated (censored) Poisson–Ailamujia models in comparison to some alternatives.

Citation

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Yasin Altinisik. Emel Cankaya. "New zero-inflated regression models with a variant of censoring." Braz. J. Probab. Stat. 36 (4) 641 - 674, December 2022. https://doi.org/10.1214/22-BJPS544

Information

Received: 1 January 2021; Accepted: 1 July 2022; Published: December 2022
First available in Project Euclid: 21 December 2022

MathSciNet: MR4524512
zbMATH: 07644486
Digital Object Identifier: 10.1214/22-BJPS544

Keywords: Censoring , Compound Poisson , count regression , Lifetime datasets , overdispersion , Poisson–Ailamujia model , zero-inflation

Rights: Copyright © 2022 Brazilian Statistical Association

Vol.36 • No. 4 • December 2022
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