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dc.contributor.authorBasturk, Nalan
dc.contributor.authorGrassi, Stefano
dc.contributor.authorHoogerheide, Lennart
dc.contributor.authorOpschoor, Anne
dc.contributor.authorvan Dijk, Herman K.
dc.date.accessioned2018-04-24T08:00:08Z
dc.date.available2018-04-24T08:00:08Z
dc.date.issued2017
dc.identifier.isbn978-82-7553-987-6
dc.identifier.issn1502-8190
dc.identifier.urihttp://hdl.handle.net/11250/2495576
dc.description.abstractThis 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 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. This occurs when the posterior or predictive density is updated with new observations and/or when one computes model probabilities using predictive likelihoods. We illustrate the MitISEM algorithm using three canonical statistical and econometric models that are characterized by several types of non-elliptical posterior shapes and that describe well-known data patterns in econometrics and finance. We show that MH using the candidate density obtained by MitISEM outperforms, in terms of numerical efficiency, MH using a simpler candidate, as well as the Gibbs sampler. The MitISEM approach is also used for Bayesian model comparison using predictive likelihoods.nb_NO
dc.language.isoengnb_NO
dc.publisherNorges Banknb_NO
dc.relation.ispartofseriesWorking Papers;10/2017
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.subjectMCMCnb_NO
dc.subjectStudent-t densitiesnb_NO
dc.subjectMetropolis-Hastings algorithmnb_NO
dc.subjectBayesian inferencenb_NO
dc.subjectR softwarenb_NO
dc.subjectexpectation maximizationnb_NO
dc.subjectfinite mixturesnb_NO
dc.subjectimportance samplingnb_NO
dc.titleThe R Package Mitisem: Efficient and Robust Simulation Procedures for Bayesian Inferencenb_NO
dc.typeWorking papernb_NO
dc.description.versionpublishedVersionnb_NO
dc.subject.nsiVDP::Samfunnsvitenskap: 200::Økonomi: 210::Samfunnsøkonomi: 212nb_NO
dc.source.pagenumber41nb_NO


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
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