Adaptive mixture of Studentt distributions as a flexible candidate distribution for efficient simulation: the R package AdMit
Published in:
 Journal of Statistical Software.  2009, vol. 29, no. 3, p. 132
English
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 given only a kernel of the target density function. The core algorithm consists of the function AdMit which fits an adaptive mixture of Studentt distributions to the density of interest via its kernel function. 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 timeconsuming 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 logreturns data. The methodology is compared to standard cases of importance sampling and the MetropolisHastings algorithm using a naive candidate and with the GriddyGibbs approach.

Faculty
 Faculté des sciences économiques et sociales

Department
 Département d'économie quantitative

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Economics

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https://folia.unifr.ch/unifr/documents/302725
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