Bayesian nonparametric predictions for count time series
نویسندگان
چکیده
In this paper we introduce a Bayesian nonparametric methodology for producing coherent predictions of count time series using the INAR(1) process. Our predictions are based on estimates of the p-step ahead predictive mass functions assuming a nonparametric prior for the distribution of the error term having large support on the space of discrete probability mass functions. An efficient Gibbs sampler is developed for posterior computation.
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تاریخ انتشار 2012