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Adaptive mixture of Student-t distributions as a flexible candidate distribution for efficient simulation: the R package AdMit

    2009
Published in:
  • Journal of Statistical Software. - 2009, vol. 29, no. 3, p. 1-32
adaptive mixture
Student-t distributions
importance sampling
independence chain Metropolis-Hastings algorithm
Bayesian inference
R software
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 Student-t 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 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.
Faculty
Faculté des sciences économiques et sociales et du management
Department
Département d'économie quantitative
Language
  • English
Classification
Economics
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/302725
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