Review of Zero-Inflated Models with Missing Data
- 1 Institute of Statistical Science, Taiwan
- 2 Feng Chia University, Taiwan
- 3 University of California, United States
Copyright: © 2020 T. Martin Lukusa, Shen-Ming Lee and Chin-Shang Li. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The literature of count regression models covers a large scope of studies and applications that implemented simple and standard models for count response variables by using Poisson regression models, binomial regression models, negative binomial regression models, geometric regression models, or generalized Poisson regression models. These regression models have received considerable attention in various situations. Nevertheless in many fields, the distribution of the count response variable may display a feature of excess zeros for which the aforementioned regression models may fail to provide an adequate fit. To remedy this handicap, a class of distributions known as zero-inflated models is considered as the most appropriate approach for dealing properly with this issue of excess zeros. In addition to the zero-inflated problem, it happens quite often that the sample data sets under investigation are not completely observed. This refers to the missing data problem. In this study, our primary interest is in reviewing studies that considered simultaneously the missing data problem and the zero-inflated feature in modeling zero-inflated data. Moreover, we discuss their methodologies and results and some potential directions of the future research.
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- Count Data
- Estimating Methods
- Missing at Random
- Missing Completely at Random
- Missing Data
- Missing Not at Random
- Zero-Inflated Models