Journal article
State-space models for count time series with excess zeros
Statistical modelling, Vol.15(1), pp.70-90
2014
DOI: 10.1177/1471082x14535530
Abstract
Count time series are frequently encountered in biomedical, epidemiological and public health applications. In principle, such series may exhibit three distinctive features: overdispersion, zero-inflation and temporal correlation. Developing a modelling framework that is sufficiently general to accommodate all three of these characteristics poses a challenge. To address this challenge, we propose a flexible class of dynamic models in the state-space framework. Certain models that have been previously introduced in the literature may be viewed as special cases of this model class. For parameter estimation, we devise a Monte Carlo Expectation-Maximization (MCEM) algorithm, where particle filtering and particle smoothing methods are employed to approximate the high-dimensional integrals in the E-step of the algorithm. To illustrate the proposed methodology, we consider an application based on the evaluation of a participatory ergonomics intervention, which is designed to reduce the incidence of workplace injuries among a group of hospital cleaners. The data consists of aggregated monthly counts of work-related injuries that were reported before and after the intervention.
Details
- Title: Subtitle
- State-space models for count time series with excess zeros
- Creators
- Ming Yang - Department of Biostatistics, Harvard School of Public Health, Boston, MA, USAJoseph E Cavanaugh - University of Iowa, BiostatisticsGideon K D Zamba - University of Iowa, Biostatistics
- Resource Type
- Journal article
- Publication Details
- Statistical modelling, Vol.15(1), pp.70-90
- DOI
- 10.1177/1471082x14535530
- ISSN
- 1477-0342
- eISSN
- 1477-0342
- Publisher
- SAGE Publications
- Language
- English
- Date published
- 2014
- Academic Unit
- Statistics and Actuarial Science; Radiology; Biostatistics; Injury Prevention Research Center
- Record Identifier
- 9983985979402771
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