Bayesian nonparametric forecasting for INAR models

作     者：Bisaglia, Luisa Canale, Antonio 

作者机构：Univ Turin Dept Econ & Stat Corso Unione Sovietica 218bis I-10134 Turin Italy Collegio Carlo Alberto Moncalieri Italy Univ Padua Dept Stat Sci I-35100 Padua Italy 

出 版 物：《COMPUTATIONAL STATISTICS & DATA ANALYSIS》 (计算统计学与数据分析)

年 卷 期：2016年第100卷

页      面：70-78页

核心收录：

学科分类：08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：University of Padua [CPDA097208/09] 

主　　题：Count time series INAR(1) Dirichlet process mixtures Forecasting Gibbs sampling algorithm 

摘      要：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. (C) 2015 Elsevier B.V. All rights reserved.