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Markov zero-inflated Poisson regression models for a time series of counts with excess zeros

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  • Peiming Wang

Abstract

This paper discusses a class of Markov zero-inflated Poisson regression models for a time series of counts with the presence of excess zero relative to a Poisson distribution, in which the frequency distribution changes according to an underlying two-state Markov chain. Features of the proposed model, estimation method based on the EM and quasi-Newton algorithms, and other implementation issues are discussed. A Monte Carlo study shows that the estimation method is accurate and reliable as long as the sample size is reasonably large, and the choice of starting probabilities for the Markov process has little impact on the parameter estimates. The methodology is illustrated using daily numbers of phone calls reporting faults for a mainframe computer system.

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  • Peiming Wang, 2001. "Markov zero-inflated Poisson regression models for a time series of counts with excess zeros," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(5), pages 623-632.
    • Handle: RePEc:taf:japsta:v:28:y:2001:i:5:p:623-632
    DOI: 10.1080/02664760120047951
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    Cited by:

    1. Dirk Douwes‐Schultz & Alexandra M. Schmidt, 2022. "Zero‐state coupled Markov switching count models for spatio‐temporal infectious disease spread," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 589-612, June.
    2. E. Andres Houseman & Brent Coull & James Shine, 2004. "A Nonstationary Negative Binomial Time Series with Time-Dependent Covariates: Enterococcus Counts in Boston Harbor," Harvard University Biostatistics Working Paper Series 1017, Berkeley Electronic Press.

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