Arbeitspapier
The R-package MitISEM: Efficient and Robust Simulation Procedures for Bayesian Inference
This paper presents the R-package MitISEM (mixture of t by importance sampling weighted expectation maximization) which provides an automatic and flexible two-stage method to approximate a non-elliptical target density kernel -- typically a posterior density kernel -- using an adaptive mixture of Student-t densities as approximating density. In the first stage a mixture of Student-t densities is fitted to the target using an expectation maximization (EM) algorithm where each step of the optimization procedure is weighted using importance sampling. In the second stage this mixture density is a candidate density for efficient and robust application of importance sampling or the Metropolis-Hastings (MH) method to estimate properties of the target distribution. The package enables Bayesian inference and prediction on model parameters and probabilities, in particular, for models where densities have multi-modal or other non-elliptical shapes like curved ridges. These shapes occur in research topics in several scientific fields. For instance, analysis of DNA data in bio-informatics, obtaining loans in the banking sector by heterogeneous groups in financial economics and analysis of education's effect on earned income in labor economics. The package MitISEM provides also an extended algorithm, 'sequential MitISEM', which substantially decreases computation time when the target density has to be approximated for increasing data samples.
- Language
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Englisch
- Bibliographic citation
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Series: Tinbergen Institute Discussion Paper ; No. 15-042/III
- Classification
-
Wirtschaft
Econometrics
Bayesian Analysis: General
Econometric Software
- Subject
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finite mixtures
Student-t densities
importance sampling
MCMC
Metropolis-Hastings algorithm
expectation maximization
Bayesian inference
R-software
- Event
-
Geistige Schöpfung
- (who)
-
Basturk, Nalan
Grassi, Stefano
Hoogerheide, Lennart
Opschoor, Anne
van Dijk, Herman K.
- Event
-
Veröffentlichung
- (who)
-
Tinbergen Institute
- (where)
-
Amsterdam and Rotterdam
- (when)
-
2015
- Handle
- Last update
-
10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Basturk, Nalan
- Grassi, Stefano
- Hoogerheide, Lennart
- Opschoor, Anne
- van Dijk, Herman K.
- Tinbergen Institute
Time of origin
- 2015
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Robust Bayesian inference in proxy SVARs
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Ensemble MCMC sampling for robust Bayesian inference
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Robust Bayesian inference in stochastic frontier models
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