Adaptive mixture of Student-t distributions as a flexible distribution for efficient simulation: The R package AdMit
David Ardia, Lennart Hoogerheide & Herman Van Dijk
| Résumé |
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. |
| Mots-clés |
Adaptive mixture, Student-t distributions, importance sampling, independence chain Metropolis-Hasting algorithm, Bayesian, R software |
| Citation | Ardia, D., Hoogerheide, L., & Van Dijk , H. (2009). Adaptive mixture of Student-t distributions as a flexible distribution for efficient simulation: The R package AdMit. Journal of Statistical Software, 29(3), 1-32. |
| Type | Article de périodique (Anglais) |
| Date de publication | 2009 |
| Nom du périodique | Journal of Statistical Software |
| Volume | 29 |
| Numéro | 3 |
| Pages | 1-32 |
| URL | https://www.jstatsoft.org/article/view/v029i03 |
| Liée au projet | Bayesian estimation of regime-switching GARCH models |