Year: 2023
Type: preprint
Abstract: Guerrero et al. \cite{GBSO} propose a novel approach to building first-order integer-valued autoregressive (\inar1) models based on the concept of thinning. The standard approach requires that the thinning operator be defined first and \inar1 models with either a specified marginal (the forward approach) or a specified innovation (the backward approach) are developed. In contrast, the approach in \cite{GBSO} is to start out by specifying both the marginal distribution of the process and that of its innovation sequence, and then proceed to identify the thinning operator by solving a functional equation. In this article we discuss the connection between the thinning operators the authors obtained for their novel geometric and negative binomial \inar1 models and the thinning operator introduced in \cite{AB1} and \cite{AB2}. More specifically, we show that the existence of the two models has been established in \cite{AB1} using the forward approach and a different parameterization. In the process, we strenghthen some of the authors' results obtained for the novel geometric \inar1 process and we extend their results to the novel negative binomial \inar1 process.
Source: arXiv (Cornell University)
Author: Bouzar, Nadjib
Institution: University of Indianapolis
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4386555909