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  1. Richard Krutchkoff (Ed.)
    Free, publicly-accessible full text available February 14, 2025
  2. Robert Taylor (Ed.)
    Free, publicly-accessible full text available February 14, 2025
  3. IMS (Ed.)
    Free, publicly-accessible full text available February 14, 2025
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  8. Donna LaLonde and Wendy Martinez (Ed.)
    Free, publicly-accessible full text available April 3, 2024
  9. Count time series are widely encountered in practice. As with continuous valued data, many count series have seasonal properties. This article uses a recent advance in stationary count time series to develop a general seasonal count time series modeling paradigm. The model constructed here permits any marginal distribution for the series and the most flexible autocorrelations possible, including those with negative dependence. Likelihood methods of inference are explored. The article first develops the modeling methods, which entail a discrete transformation of a Gaussian process having seasonal dynamics. Properties of this model class are then established and particle filtering likelihood methods of parameter estimation are developed. A simulation study demonstrating the efficacy of the methods is presented and an application to the number of rainy days in successive weeks in Seattle, Washington is given.

     
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