Adaptive Mixture of Student-t Distributions as a Flexible Candidate Distribution for Efficient Simulation: The R Package AdMit
Lennart F. Hoogerheide and
Herman van Dijk ()
Journal of Statistical Software, 2009, vol. 029, issue i03
This paper presents the R package AdMit which provides flexible functions to approximate a certain target distribution and to efficiently generate a sample of random draws from it, given only a kernel of the target density function. The core algorithm consists of the function AdMit which fits an adaptive mixture of Student-t distributions to the density of interest. Then, importance sampling or the independence chain Metropolis-Hastings algorithm is used to obtain quantities of interest for the target density, using the fitted mixture as the importance or candidate density. The estimation procedure is fully automatic and thus avoids the time-consuming and difficult task of tuning a sampling algorithm. The relevance of the package is shown in two examples. The first aims at illustrating in detail the use of the functions provided by the package in a bivariate bimodal distribution. The second shows the relevance of the adaptive mixture procedure through the Bayesian estimation of a mixture of ARCH model fitted to foreign exchange log-returns data. The methodology is compared to standard cases of importance sampling and the Metropolis-Hastings algorithm using a naive candidate and with the Griddy-Gibbs approach.
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16) Track citations by RSS feed
Downloads: (external link)
https://www.jstatsoft.org/index.php/jss/article/do ... AdMit_1-01.01.tar.gz
https://www.jstatsoft.org/index.php/jss/article/do ... v029i03/v29i03.R.zip
Working Paper: Adaptive mixture of Student-t distributions as a flexible candidate distribution for efficient simulation: the R package AdMit (2009)
Working Paper: Adaptive Mixture of Student-t distributions as a Flexible Candidate Distribution for Efficient Simulation: the R Package AdMit (2008)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:029:i03
Access Statistics for this article
Journal of Statistical Software is currently edited by Bettina Grün, Edzer Pebesma and Achim Zeileis
More articles in Journal of Statistical Software from Foundation for Open Access Statistics
Bibliographic data for series maintained by Christopher F. Baum ().