A new First-Order mixture integer-valued threshold autoregressive process based on binomial thinning and negative binomial thinning

Autor:	Sheng, Danshu, Wang, Dehui, Sun, Liuquan
Rok vydání:	2023
Předmět:	Statistics - Applications Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	In this paper, we introduce a new first-order mixture integer-valued threshold autoregressive process, based on the binomial and negative binomial thinning operators. Basic probabilistic and statistical properties of this model are discussed. Conditional least squares (CLS) and conditional maximum likelihood (CML) estimators are derived and the asymptotic properties of the estimators are established. The inference for the threshold parameter is obtained based on the CLS and CML score functions. Moreover, the Wald test is applied to detect the existence of the piecewise structure. Simulation studies are considered, along with an application: the number of criminal mischief incidents in the Pittsburgh dataset. Comment: 34 pages;5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2309.01862 Zobrazit plný text záznamu View this record from Arxiv