Journal article
Markov regression models for count time series with excess zeros: A partial likelihood approach
Statistical methodology, Vol.14, pp.26-38
09/2013
DOI: 10.1016/j.stamet.2013.02.001
Abstract
Count data with excess zeros are common in many biomedical and public health applications. The zero-inflated Poisson (ZIP) regression model has been widely used in practice to analyze such data. In this paper, we extend the classical ZIP regression framework to model count time series with excess zeros. A Markov regression model is presented and developed, and the partial likelihood is employed for statistical inference. Partial likelihood inference has been successfully applied in modeling time series where the conditional distribution of the response lies within the exponential family. Extending this approach to ZIP time series poses methodological and theoretical challenges, since the ZIP distribution is a mixture and therefore lies outside the exponential family. In the partial likelihood framework, we develop an EM algorithm to compute the maximum partial likelihood estimator (MPLE). We establish the asymptotic theory of the MPLE under mild regularity conditions and investigate its finite sample behavior in a simulation study. The performances of different partial-likelihood based model selection criteria are compared in the presence of model misspecification. Finally, we present an epidemiological application to illustrate the proposed methodology.
Details
- Title: Subtitle
- Markov regression models for count time series with excess zeros: A partial likelihood approach
- Creators
- Ming Yang - Center for Biostatistics in AIDS Research, Harvard School of Public Health, United StatesGideon K.D Zamba - Department of Biostatistics, College of Public Health, The University of Iowa, United StatesJoseph E Cavanaugh - Department of Biostatistics, College of Public Health, The University of Iowa, United States
- Resource Type
- Journal article
- Publication Details
- Statistical methodology, Vol.14, pp.26-38
- DOI
- 10.1016/j.stamet.2013.02.001
- ISSN
- 1572-3127
- eISSN
- 1878-0954
- Publisher
- Elsevier B.V
- Language
- English
- Date published
- 09/2013
- Academic Unit
- Statistics and Actuarial Science; Radiology; Biostatistics; Injury Prevention Research Center
- Record Identifier
- 9984214806902771
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