期刊
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
卷 38, 期 3, 页码 638-653出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/03610910802601332
关键词
AR(1), MA(1), and exchangeable correlation structures; Generalized quasi-likelihood; Non stationary; Zero-inflated Poisson
资金
- Natural Sciences and Engineering Research Council of Canada
- University of New Brunswick
Medical and public health research often involve the analysis of repeated or longitudinal count data that exhibit excess zeros such as the number of yearly doctor visits by a group of individuals over a number of years. Zero-inflated Poisson (ZIP) regression models can be used to account for excess zeros in count data. We propose an extension of the ZIP model that is appropriate for longitudinal data. Our extension includes a non stationary, observation-driven time series model based correlation structure. We discuss estimation of the model parameters and the inefficiency of the estimators when the correlation structure is mis-specified. The model's application to the analysis of health care utilization data is also discussed.
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