Quotation Frühwirth-Schnatter, Sylvia. 1995. Bayesian model discrimination and Bayes factorsfor linear Gaussian state space models. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 57 237-246.


RIS


BibTeX

Abstract

It is shown how to discriminate between different linear Gaussian state space models for a given time series by means of a Bayesian approach which chooses the model that minimizes the expected loss. A practical implementation of this procedure requires a fully Bayesian analysis for both the state vector and the unknown hyperparameters and is carried out by Markov chain Monte Carlo methods. An application to some non-standard situations such as testing hypothesis on the boundary of the parameter space, discriminating non-nested models and discrimination of more than two models is discussed in detail.

Tags

Press 'enter' for creating the tag

Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Journal of the Royal Statistical Society: Series B (Statistical Methodology)
Citation Index SCI
WU Journalrating 2009 A
WU-Journal-Rating new FIN-A, VW-A, WH-A
Language English
Title Bayesian model discrimination and Bayes factorsfor linear Gaussian state space models
Volume 57
Year 1995
Page from 237
Page to 246
URL http://www.jstor.org/stable/view/2346097
DOI http://dx.doi.org/10.1111/j.2517-6161.1995.tb02027.x
Open Access N

Associations

People
Frühwirth-Schnatter, Sylvia (Details)
Organization
Institute for Statistics and Mathematics IN (Details)
Google Scholar: Search