AdMit
Adaptive Mixture of Student-t Distributions as a Flexible Candidate Distribution for Efficient Simulation: The R Package AdMit. 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.
Keywords for this software
References in zbMATH (referenced in 8 articles , 1 standard article )
Showing results 1 to 8 of 8.
Sorted by year (- David Ardia; Keven Bluteau; Kris Boudt; Leopoldo Catania; Denis-Alexandre Trottier: Markov-Switching GARCH Models in R: The MSGARCH Package (2019) not zbMATH
- Geweke, John; Durham, Garland: Sequentially adaptive Bayesian learning algorithms for inference and optimization (2019)
- Dinh, Vu; Rundell, Ann E.; Buzzard, Gregery T.: Convergence of Griddy Gibbs sampling and other perturbed Markov chains (2017)
- Nalan Baştürk and Stefano Grassi and Lennart Hoogerheide and Anne Opschoor and Herman van Dijk: The R Package MitISEM: Efficient and Robust Simulation Procedures for Bayesian Inference (2017) not zbMATH
- Kleppe, Tore Selland; Liesenfeld, Roman: Efficient importance sampling in mixture frameworks (2014)
- Ardia, David; Baştürk, Nalan; Hoogerheide, Lennart; Van Dijk, Herman K.: A comparative study of Monte Carlo methods for efficient evaluation of marginal likelihood (2012)
- Hautsch, Nikolaus; Yang, Fuyu: Bayesian inference in a stochastic volatility Nelson-Siegel model (2012)
- David Ardia; Lennart Hoogerheide; Herman van Dijk: Adaptive Mixture of Student-t Distributions as a Flexible Candidate Distribution for Efficient Simulation: The R Package AdMit (2009) not zbMATH