Maximum-Likelihood Estimation for the Zero-Inflated Polynomial-Adjusted Poisson Distribution

We propose the zero-inflated Polynomially Adjusted Poisson (zPAP) model. It extends the usual zero-inflated Poisson by multiplying the Poisson kernel with a nonnegative polynomial, enabling the model to handle extra zeros, overdispersion, skewness, and even multimodal counts. We derive the maximum-l...

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Main Authors: Jong-Seung Lee, Hyung-Tae Ha
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Mathematics
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Online Access:https://www.mdpi.com/2227-7390/13/15/2383
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author Jong-Seung Lee
Hyung-Tae Ha
author_facet Jong-Seung Lee
Hyung-Tae Ha
author_sort Jong-Seung Lee
collection DOAJ
description We propose the zero-inflated Polynomially Adjusted Poisson (zPAP) model. It extends the usual zero-inflated Poisson by multiplying the Poisson kernel with a nonnegative polynomial, enabling the model to handle extra zeros, overdispersion, skewness, and even multimodal counts. We derive the maximum-likelihood framework—including the log-likelihood and score equations under both general and regression settings—and fit zPAP to the zero-inflated, highly dispersed Fish Catch data as well as a synthetic bimodal mixture. In both cases, zPAP not only outperforms the standard zero-inflated Poisson model but also yields reliable inference via parametric bootstrap confidence intervals. Overall, zPAP is a clear and tractable tool for real-world count data with complex features.
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spelling doaj-art-38c0551c6ebd48c9b9e19bd040bfa4832025-08-20T04:00:50ZengMDPI AGMathematics2227-73902025-07-011315238310.3390/math13152383Maximum-Likelihood Estimation for the Zero-Inflated Polynomial-Adjusted Poisson DistributionJong-Seung Lee0Hyung-Tae Ha1Department of Next Generation Smart Energy System Convergence, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si 13557, Gyeonggi-do, Republic of KoreaDepartment of Next Generation Smart Energy System Convergence, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si 13557, Gyeonggi-do, Republic of KoreaWe propose the zero-inflated Polynomially Adjusted Poisson (zPAP) model. It extends the usual zero-inflated Poisson by multiplying the Poisson kernel with a nonnegative polynomial, enabling the model to handle extra zeros, overdispersion, skewness, and even multimodal counts. We derive the maximum-likelihood framework—including the log-likelihood and score equations under both general and regression settings—and fit zPAP to the zero-inflated, highly dispersed Fish Catch data as well as a synthetic bimodal mixture. In both cases, zPAP not only outperforms the standard zero-inflated Poisson model but also yields reliable inference via parametric bootstrap confidence intervals. Overall, zPAP is a clear and tractable tool for real-world count data with complex features.https://www.mdpi.com/2227-7390/13/15/2383zero-inflated poissonpolynomially adjusted poissonmaximum likelihood estimationmultimodalitycount regressionfish catch dataset
spellingShingle Jong-Seung Lee
Hyung-Tae Ha
Maximum-Likelihood Estimation for the Zero-Inflated Polynomial-Adjusted Poisson Distribution
Mathematics
zero-inflated poisson
polynomially adjusted poisson
maximum likelihood estimation
multimodality
count regression
fish catch dataset
title Maximum-Likelihood Estimation for the Zero-Inflated Polynomial-Adjusted Poisson Distribution
title_full Maximum-Likelihood Estimation for the Zero-Inflated Polynomial-Adjusted Poisson Distribution
title_fullStr Maximum-Likelihood Estimation for the Zero-Inflated Polynomial-Adjusted Poisson Distribution
title_full_unstemmed Maximum-Likelihood Estimation for the Zero-Inflated Polynomial-Adjusted Poisson Distribution
title_short Maximum-Likelihood Estimation for the Zero-Inflated Polynomial-Adjusted Poisson Distribution
title_sort maximum likelihood estimation for the zero inflated polynomial adjusted poisson distribution
topic zero-inflated poisson
polynomially adjusted poisson
maximum likelihood estimation
multimodality
count regression
fish catch dataset
url https://www.mdpi.com/2227-7390/13/15/2383
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