Efficient Bayesian estimation of multivariate state space models

被引：11

作者：

Strickland, Chris M. ^{[1
]}

Turner, Ian. W. ^{[1
]}

Denham, Robert ^{[2
]}

Mengersen, Kerrie L. ^{[1
]}

机构：

[1] Queensland Univ Technol, Sch Math, Brisbane, Qld 4001, Australia

[2] Ctr Remote Sensing, Dept Environm & Resource Management, Indooroopilly, Qld 4068, Australia

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2009年 / 53卷 / 12期

基金：

澳大利亚研究理事会;

关键词：

TIME-SERIES; SIMULATION; CYCLES;

D O I：

10.1016/j.csda.2009.04.019

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

A Bayesian Markov chain Monte Carlo methodology is developed for the estimation of multivariate linear Gaussian state space models. In particular, an efficient simulation smoothing algorithm is proposed that makes use of the univariate representation of the state space model. Substantial gains over existing algorithms in computational efficiency are achieved using the new simulation smoother for the analysis of high dimensional multivariate time series. The methodology is used to analyse a multivariate time series dataset of the Normalised Difference Vegetation Index (NDVI), which is a proxy for the level of live vegetation, for a particular grazing property located in Queensland, Australia. (C) 2009 Elsevier B.V. All rights reserved.

引用

页码：4116 / 4125

页数：10

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