Binomial AR(1) processes with innovational outliers. Issue 2 (17th January 2021)

• Record Type: Journal Article
• Authors: Chen, Huaping; Li, Qi; Zhu, Fukang
• Is Part Of: Communications in statistics. Volume 50:Issue 2(2021)
• Page Start: 446
• Page End: 472
• Publication Date: 2021-01-17
• Subjects: Time series of counts -- binomial autoregression -- outlier -- parameter estimation; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2019.1635704
• Languages: English
• ISSNs: 0361-0926

Abstract

Abstract: Binomial integer-valued AR processes have been well studied in the literature, but there is little progress in modeling bounded integer-valued time series with outliers. In this paper, we first review some basic properties of the binomial integer-valued AR(1) process and then we introduce binomial integer-valued AR(1) processes with two classes of innovational outliers. We focus on the joint conditional least squares (CLS) and the joint conditional maximum likelihood (CML) estimates of models' parameters and the probability of occurrence of the outlier. Their large-sample properties are illustrated by simulation studies. Artificial and real data examples are used to demonstrate good performances of the proposed models.