Binomial AR(1) processes: moments, cumulants, and estimation
- Authors
- Weiss, Christian H.; Kim, Hee-Young
- Issue Date
- 1-6월-2013
- Publisher
- TAYLOR & FRANCIS LTD
- Keywords
- binomial AR(1) model; conditional least-squares estimator; cumulants; moments; squared difference estimator; 60J10; 62F12; 62M05; 62M10
- Citation
- STATISTICS, v.47, no.3, pp.494 - 510
- Indexed
- SCIE
SCOPUS
- Journal Title
- STATISTICS
- Volume
- 47
- Number
- 3
- Start Page
- 494
- End Page
- 510
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/102995
- DOI
- 10.1080/02331888.2011.605893
- ISSN
- 0233-1888
- Abstract
- The modelling and analysis of count-data time series are areas of emerging interest with various applications in practice. We consider the particular case of the binomial AR(1) model, which is well suited for describing binomial counts with a first-order autoregressive serial dependence structure. We derive explicit expressions for the joint (central) moments and cumulants up to order 4. Then, we apply these results for expressing moments and asymptotic distribution of the squared difference estimator as an alternative to the sample autocovariance. We also analyse the asymptotic distribution of the conditional least-squares estimators of the parameters of the binomial AR(1) model. The finite-sample performance of these estimators is investigated in a simulation study, and we apply them to real data about computerized workstations.
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Collections - College of Public Policy > Division of Big Data Science > 1. Journal Articles
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