首页 | 本学科首页   官方微博 | 高级检索  
     检索      


Posterior consistency for the spectral density of non-Gaussian stationary time series
Authors:Yifu Tang  Claudia Kirch  Jeong Eun Lee  Renate Meyer
Institution:1. Department of Statistics, The University of Auckland, Auckland, New Zealand;2. Department of Mathematics, Institute for Mathematical Stochastics, Otto-von-Guericke University, Magdeburg, Germany
Abstract:Various nonparametric approaches for Bayesian spectral density estimation of stationary time series have been suggested in the literature, mostly based on the Whittle likelihood approximation. A generalization of this approximation involving a nonparametric correction of a parametric likelihood has been proposed in the literature with a proof of posterior consistency for spectral density estimation in combination with the Bernstein–Dirichlet process prior for Gaussian time series. In this article, we will extend the posterior consistency result to non-Gaussian time series by employing a general consistency theorem for dependent data and misspecified models. As a special case, posterior consistency for the spectral density under the Whittle likelihood is also extended to non-Gaussian time series. Small sample properties of this approach are illustrated with several examples of non-Gaussian time series.
Keywords:Bayesian consistency  Bayesian nonparametrics  spectral density function  stationary time series
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号