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INAR(1)-PK: ‎Modeling Discrete-Time Count Data with Overdispersion
Mahdi Rasekhi *
Abstract:   (27 Views)

In this paper, a first-order integer-valued autoregressive process with non-negative integer values is introduced, based on the binomial thinning operator and driven by Poisson-Komal distributed noise. To estimate the parameters of the proposed model, two estimation methods are investigated: Conditional Maximum Likelihood Estimation and the Yule–Walker Method. Furthermore, the performance of these estimation techniques is evaluated through a simulation study. In addition, the practical applicability of the proposed model is demonstrated using two real-world datasets from the field of veterinary sciences.

Keywords: Poisson-Komal distribution‎, ‎INAR(1) process‎, ‎Conditional Maximum Likelihood‎, ‎Binomial thinning operator‎, ‎Yule–Walker estimate.
Full-Text [PDF 4726 kb]   (33 Downloads)    
Type of Study: Research | Subject: Time Series
Received: 2025/06/29 | Accepted: 2026/09/1 | Published: 2026/09/1
References
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