Bayesian and frequentist approaches on estimation and testing for a zero-inflated binomial distribution
- Authors
- Nam, Seungji; Kim, Seong W.; Ng, Hon Keung Tony
- Issue Date
- Jun-2022
- Publisher
- University of Hacettepe
- Keywords
- Bayes factor; binomial distribution; EM algorithm; Jeffreys prior; maximum likelihood estimate; zero-inflated models
- Citation
- Hacettepe Journal of Mathematics and Statistics, v.51, no.3, pp.834 - 856
- Indexed
- SCIE
SCOPUS
- Journal Title
- Hacettepe Journal of Mathematics and Statistics
- Volume
- 51
- Number
- 3
- Start Page
- 834
- End Page
- 856
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/111526
- DOI
- 10.15672/hujms.959817
- ISSN
- 1303-5010
- Abstract
- To analyze discrete count data with excessive zeros, different zero-inflated statistical models that allow for frequent zero-valued observations have been developed. When the underlying data generation process of non-zero values is based on the number of successes in a sequence of independent Bernoulli trials, the zero-inflated binomial distribution is perhaps adequate for modeling purposes. In this paper, we discuss statistical inference for a zero-inflated binomial distribution using the objective Bayesian and frequentist approaches. Point and interval estimation of the model parameters and hypothesis testing for excessive zeros in a zero-inflated binomial distribution are developed. A Monte Carlo simulation study is used to assess the performance of estimation and hypothesis testing procedures. A comparative study of the objective Bayesian approach and the frequentist approach is provided. The proposed statistical inferential methods are applied to analyze an earthquake dataset and a baseball dataset for illustration.
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