Bayesian Models for Zero Truncated Count Data. Asian Journal of Probability and Statistics.

Adesina, Olumide S and Agunbiade, D.A and Oguntunde, P.E. and Adesina, Tolulope F (2019) Bayesian Models for Zero Truncated Count Data. Asian Journal of Probability and Statistics. Asian Journal of Probability and Statistics, 4 (1). pp. 1-12.

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Abstract

It is important to fit count data with suitable model(s), models such as Poisson Regression, Quassi Poisson, Negative Binomial, to mention but a few have been adopted by researchers to fit zero truncated count data in the past. In recent times, dedicated models for fitting zero truncated count data have been developed, and they are considered sufficient. This study proposed Bayesian multi-level Poisson and Bayesian multi-level Geometric model, Bayesian Monte Carlo Markov Chain Generalized linear Mixed Models (MCMCglmms) of zero truncated Poisson and MCMCglmms Poisson regression model to fit health count data that is truncated at zero. Suitable model selection criteria were used to determine preferred models for fitting zero truncated data. Results obtained showed that Bayesian multi-level Poisson outperformed Bayesian multi-level Poisson Geometric model; also MCMCglmms of zero truncated Poisson outperformed MCMCglmms Poisson.

Item Type:	Article
Uncontrolled Keywords:	Count data; Bayesian inference; health insurance; zero-truncated; multi-level models
Subjects:	H Social Sciences > HA Statistics H Social Sciences > HG Finance
Divisions:	Faculty of Law, Arts and Social Sciences > School of Social Sciences
Depositing User:	Dr. Tolulope Adesina (Oladeji)
Date Deposited:	18 May 2021 12:55
Last Modified:	18 May 2021 12:55
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/14115

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