Document Type

Article

Publication Date

2023

DOI

10.3390/analytics2010014

Publication Title

Analytics

Volume

2

Issue

1

Pages

265-283

Abstract

In health and social science and other fields where count data analysis is important, zero-inflated models have been employed when the frequency of zero count is high (inflated). Due to multiple reasons, there are scenarios in which an additional count value of k > 0 occurs with high frequency. The zero- and k-inflated Poisson distribution model (ZkIP) is more appropriate for such situations. The ZkIP model is a mixture distribution with three components: degenerate distributions at 0 and k count and a Poisson distribution. In this article, we propose an alternative and computationally fast expectation–maximization (EM) algorithm to obtain the parameter estimates for grouped zero and k-inflated count data. The asymptotic standard errors are derived using the complete data approach. We compare the zero- and k-inflated Poisson model with its zero-inflated and non-inflated counterparts. The best model is selected based on commonly used criteria. The theoretical results are supplemented with the analysis of two real-life datasets from health sciences.

Rights

© 2023 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: The data that support the findings of this study are openly available in “Data Files” at https://www.cdc.gov/nchs/nhis/1997-2018.htm (accessed on 27 May 2022)

Original Publication Citation

Arora, M., & Chaganty, N. R. (2023). Application of mixture models for doubly inflated count data. Analytics, 2(1), 265-283. https://doi.org/10.3390/analytics2010014

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