Observation-Driven Model for Zero-Inflated Daily Counts of Emergency Room Visit Data
Time series data with excessive zeros frequently occur in medical and health studies. To analyze time series count data without excessive zeros, observation-driven Poisson regression models are commonly used in the literature. As handling excessive zeros in count data is not straightforward, observation-driven models are rarely used to analyze time series count data with excessive zeros. In this paper an observation-driven zero-inflated Poisson (ZIP) model for time series count data is proposed. This approach can accommodate an autoregressive serial dependence structure which commonly appears in time series. The estimation of the model parameters by using the quasi-likelihood estimating equation approach is discussed. To estimate the correlation parameters of the dependence structure, a moment approach is used. The proposed methodology is illustrated by applying it to a data set of daily emergency room visits due to bronchitis.
Autocorrelation structure, non-stationary, observation-driven model, quasi-likelihood, zero-inflated Poisson
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