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

Peiming Wang

Journal of Applied Statistics, 2001, vol. 28, issue 5, 623-632

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.

Date: 2001
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/02664760120047951 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:28:y:2001:i:5:p:623-632

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664760120047951

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().