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AdMit: Adaptive Mixtures of Student-t Distributions

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Author Info
David Ardia () (Department of Quantitative Economics)
Lennart F. Hoogerheide (Econometric Institute / Erasmus University)
Herman K. van Dijk (Econometric Institute / Erasmus University)

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Abstract

This short note 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 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.

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Publisher Info
Paper provided by Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland in its series DQE Working Papers with number 10.

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Length: 6 pages
Date of creation: 01 Aug 2008
Date of revision: 07 Jan 2009
Publication status: Published in The R Journal, 2009, vol. 1, no. 1, pp.25--31, URL http://journal.r-project.org/index.h
Handle: RePEc:fri:dqewps:wp0010

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Related research
Keywords: adaptive mixture; Student-t distributions; importance sampling; independence chain Metropolis-Hastings algorithm; Bayesian inference; R software.;

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Find related papers by JEL classification:
C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-39, November. [Downloadable!] (restricted)
  2. Hoogerheide, Lennart F. & Kaashoek, Johan F. & van Dijk, Herman K., 2007. "On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: An application of flexible sampling methods using neural networks," Journal of Econometrics, Elsevier, vol. 139(1), pages 154-180, July. [Downloadable!] (restricted)
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  3. David Ardia & Lennart F. Hoogerheide & Herman K. van Dijk, 2008. "Adaptive mixture of Student-t distributions as a flexible candidate distribution for efficient simulation: the R package AdMit," DQE Working Papers 9, Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland, revised 07 Jan 2009. [Downloadable!]
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Lennart Hoogerheide & Herman K. van Dijk, 2008. "Bayesian Forecasting of Value at Risk and Expected Shortfall using Adaptive Importance Sampling," Tinbergen Institute Discussion Papers 08-092/4, Tinbergen Institute. [Downloadable!]
  2. David Ardia & Lennart F. Hoogerheide & Herman K. van Dijk, 2008. "Adaptive Mixture of Student-t distributions as a Flexible Candidate Distribution for Efficient Simulation," Tinbergen Institute Discussion Papers 08-062/4, Tinbergen Institute, revised 15 Dec 2008. [Downloadable!]
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