SUPERPOSITIONED STATIONARY COUNT TIME SERIES. Issue 3 (July 2021)

• Record Type: Journal Article
• Authors: Jia, Yisu; Lund, Robert; Livsey, James
• Is Part Of: Probability in the engineering and informational sciences. Volume 35:Issue 3(2021)
• Page Start: 538
• Page End: 556
• Publication Date: 2021-07
• Subjects: count-valued processes -- negative binomial distribution -- Poisson distribution -- stationary processes -- time series; Probabilities -- Periodicals; Engineering -- Statistical methods -- Periodicals; Information science -- Statistical methods -- Periodicals; 519.202462
• DOI: 10.1017/S0269964819000433
• Languages: English
• ISSNs: 0269-9648

Abstract

Abstract: This paper probabilistically explores a class of stationary count time series models built by superpositioning (or otherwise combining) independent copies of a binary stationary sequence of zeroes and ones. Superpositioning methods have proven useful in devising stationary count time series having prespecified marginal distributions. Here, basic properties of this model class are established and the idea is further developed. Specifically, stationary series with binomial, Poisson, negative binomial, discrete uniform, and multinomial marginal distributions are constructed; other marginal distributions are possible. Our primary goal is to derive the autocovariance function of the resulting series.