December 2023 On quasi Pólya thinning operator
Jean Peyhardi
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Braz. J. Probab. Stat. 37(4): 643-666 (December 2023). DOI: 10.1214/23-BJPS585

Abstract

Thinning operation is a stochastic operation that shrinks a random count variable into a smaller one. This kind of random operation has been intensively studied during the seventies to characterize some count distributions, such as the Poisson distribution using the binomial thinning operator (also named binomial damage model). Then, the closure under thinning operator has been studied in order to define some classes of integer valued autoregressive (INAR) models for count time series. These two properties will be studied in this paper for the new class of quasi Pólya thinning operators. Classical results concerning the binomial thinning operator are recovered as a special case. The quasi Pólya thinning operator is related to the new class of quasi Pólya splitting distributions, defined for multivariate count data. The probabilistic graphical model (PGM) of these multivariate distributions is characterized. Finally a general class of integer valued autoregressive models is introduced, including the usual cases of Poisson marginal or generalized Poisson as a special cases and the generalized negative binomial as a new case.

Funding Statement

This research was supported by the GAMBAS project funded by the French National Research Agency (ANR-18-CE02-0025).

Citation

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Jean Peyhardi. "On quasi Pólya thinning operator." Braz. J. Probab. Stat. 37 (4) 643 - 666, December 2023. https://doi.org/10.1214/23-BJPS585

Information

Received: 1 March 2023; Accepted: 1 October 2023; Published: December 2023
First available in Project Euclid: 28 December 2023

MathSciNet: MR4682708
Digital Object Identifier: 10.1214/23-BJPS585

Keywords: binomial thinning operator , probabilistic graphical model , Quasi Polya distribution

Rights: This research was funded, in part, by the GAMBAS project funded by the French National Research Agency (ANR-18-CE02-0025). A CC BY 4.0 license is applied to this article arising from this submission, in accordance with the grant's open access conditions.

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Vol.37 • No. 4 • December 2023
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