Efficient importance sampling in mixture frameworks

Tore Kleppe () and Roman Liesenfeld

Computational Statistics & Data Analysis, 2014, vol. 76, issue C, 449-463

Abstract: A flexible importance sampling procedure for the likelihood evaluation of dynamic latent variable models involving mixtures of distributions leading to possibly heavy tailed or multi-modal target densities is provided. The procedure is based upon the efficient importance sampling (EIS) approach and exploits the mixture structure of the model via data augmentation when constructing importance sampling distributions as mixtures of distributions. The proposed mixture EIS procedure is illustrated with ML estimation of a Student-t state space model for realized volatilities. MC simulations are used to characterize the sampling distribution of the ML estimator based upon the mixture EIS approach.

Keywords: Data augmentation; Dynamic latent variable model; Importance sampling; Marginalized likelihood; Mixture; Monte Carlo; Realized volatility (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947313000406
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:76:y:2014:i:c:p:449-463

DOI: 10.1016/j.csda.2013.01.025

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().