A Class of Higher-Order INAR Random Fields for Poisson Counts and Beyond

TL;DR

This paper introduces CINAR models, a new class of higher-order INAR random fields that facilitate easier characterization of distributions and inference, with applications to Poisson and negative-binomial counts.

Contribution

The paper proposes CINAR models that combine autoregressive dependence with flexible marginal distributions, addressing key limitations of existing INAR models.

Findings

Derived stochastic properties including simple conditional probability expressions.

Developed parameter estimation methods for CINAR models.

Demonstrated practical relevance through an agricultural data application.

Abstract

Existing integer-valued autoregressive (INAR) models for count random fields suffer from difficulties in characterizing the stationary marginal distribution and in computing conditional probabilities (as required for likelihood inference). To overcome these drawbacks, the novel class of combined INAR (CINAR) models is proposed, which both exhibits the classical autoregressive dependence structure and allows to specify the marginal distribution within the wide class of discrete self-decomposable distributions. In particular, CINAR random fields can be equipped with a Poisson or negative-binomial marginal distribution. The CINAR's key stochastic properties are derived (including a simple expression for conditional probabilities), and special cases as well as possible extensions are discussed. Approaches for parameter estimation are developed and investigated, and the practical relevance of the novel CINAR family is demonstrated by an agricultural data application.