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Extended binomial AR(1) processes with generalized binomial thinning operator

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  • Yao Kang
  • Dehui Wang
  • Kai Yang

Abstract

In this article, we introduce an extended binomial AR(1) model based on the generalized binomial thinning operator. This operator relaxes the independence assumption of the binomial thinning operator and contains dependent Bernoulli counting series. The new model contains the binomial AR(1) model as a particular case. Some probabilistic and statistical properties are explored. Estimators of the model parameters are derived by conditional maximum likelihood (CML), conditional least squares (CLS) and weighted conditional least squares (WCLS) methods. Some asymptotic properties and numerical results of the estimators are studied. The good performance of the new model is illustrated, among other competitive models in the literature, by an application to the monthly drunken driving counts.

Suggested Citation

  • Yao Kang & Dehui Wang & Kai Yang, 2020. "Extended binomial AR(1) processes with generalized binomial thinning operator," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(14), pages 3498-3520, July.
    • Handle: RePEc:taf:lstaxx:v:49:y:2020:i:14:p:3498-3520
    DOI: 10.1080/03610926.2019.1589519
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    Cited by:

    1. Huaping Chen, 2023. "A New Soft-Clipping Discrete Beta GARCH Model and Its Application on Measles Infection," Stats, MDPI, vol. 6(1), pages 1-19, February.

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