mvtnorm: New numerical algorithm for multivariate normal probabilities

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dc.identifier.uri http://dx.doi.org/10.15488/3835
dc.identifier.uri https://www.repo.uni-hannover.de/handle/123456789/3869
dc.contributor.author Mi, Xuefei
dc.contributor.author Miwa, Tetsuhisa
dc.contributor.author Hothorn, Torsten
dc.date.accessioned 2018-10-11T09:16:13Z
dc.date.available 2018-10-11T09:16:13Z
dc.date.issued 2009
dc.identifier.citation Mi, X.; Miwa, T.; Hothorn, T.: mvtnorm: New numerical algorithm for multivariate normal probabilities. In: R Journal 1 (2009), Nr. 1, S. 37-39
dc.description.abstract Miwa et al. (2003) proposed a numerical algorithm for evaluating multivariate normal probabilities. Starting with version 0.9-0 of the mvtnorm package (Hothorn et al., 2001; Genz et al., 2008), this algorithm is available to the R community. We give a brief introduction to Miwa's procedure and compare it, with respect to computing time and accuracy, to a quasi-randomized Monte-Carlo procedure proposed by Genz and Bretz (1999), which has been available through mvtnorm for some years now. The new algorithm is applicable to problems with dimension smaller than 20, whereas the procedures by Genz and Bretz (1999) can be used to evaluate 1000-dimensional normal distributions. At the end of this article, a suggestion is given for choosing a suitable algorithm in different situations. eng
dc.language.iso eng
dc.publisher Vienna : The R Foundation
dc.relation.ispartofseries R Journal 1 (2009), Nr. 1
dc.rights CC BY 3.0 Unported
dc.rights.uri https://creativecommons.org/licenses/by/3.0/
dc.subject Algorithm eng
dc.subject mvtnorm eng
dc.subject Monte-Carlo eng
dc.subject.ddc 510 | Mathematik ger
dc.title mvtnorm: New numerical algorithm for multivariate normal probabilities
dc.type article
dc.type Text
dc.relation.issn 20734859
dc.bibliographicCitation.issue 1
dc.bibliographicCitation.volume 1
dc.bibliographicCitation.firstPage 37
dc.bibliographicCitation.lastPage 39
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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