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  4. AdMit: Adaptive mixtures of Student-t distributions

AdMit: Adaptive mixtures of Student-t distributions

Author(s)
Ardia, David  
Chaire de gestion des risques financiers  
Hoogerheide, Lennart
Van Dijk, Herman
Date issued
2009
In
The R Journal
Vol
1
No
1
From page
25
To page
30
Reviewed by peer
1
Subjects
Adaptive mixture Student-t distributions importance sampling independence chain Metropolis-Hasting algorithm Bayesian R software
Abstract
This short note presents the R package AdMit which provides flexible functions to approximate a certain target distribution and it provides an efficient sample of random draws from it, given only a kernel of the target density function. The estimation procedure is fully automatic and thus avoids the time-consuming and difficult task of tuning a sampling algorithm. To illustrate the use of the package, we apply the AdMit methodology to a bivariate bimodal distribution. We describe the use of the functions provided by the package and document the ability and relevance of the methodology to reproduce the shape of non-elliptical distributions.
Project(s)
Bayesian estimation of regime-switching GARCH models  
Later version
https://journal.r-project.org/archive/2009-1/RJournal_2009-1_Ardia+et+al.pdf
Publication type
journal article
Identifiers
https://libra.unine.ch/handle/20.500.14713/63536
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