کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6869077 681495 2016 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Bayesian nonparametric forecasting for INAR models
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
Bayesian nonparametric forecasting for INAR models
چکیده انگلیسی
A nonparametric Bayesian method for producing coherent predictions of count time series with the nonnegative integer-valued autoregressive process is introduced. Predictions are based on estimates of h-step-ahead predictive mass functions, assuming a nonparametric distribution for the innovation process. That is, the distribution of errors are modeled by means of a Dirichlet process mixture of rounded Gaussians. This class of prior has large support on the space and probability mass functions and can generate almost any kind of count distribution, including over/under-dispersion and multimodality. An efficient Gibbs sampler is developed for posterior computation, and the method is used to analyze a dataset of visits to a web site.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 100, August 2016, Pages 70-78
نویسندگان
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