The DOI matches its Crossref record.
Comparison of Methods for the Computation of Multivariate t Probabilities
Abstract
This article compares methods for the numerical computation of multivariate t probabilities for hyper-rectangular integration regions. Methods based on acceptance-rejection, spherical-radial transformations, and separation-of-variables transformations are considered. Tests using randomly chosen problems show that the most efficient numerical methods use a transformation developed by Genz for multivariate normal probabilities. These methods allow moderately accurate multivariate t probabilities to be quickly computed for problems with as many as 20 variables. Methods for the noncentral multivariate t distribution are also described.
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Citations
Cited by
Dose finding—a challenge in statistics.
Bretz, F., Hsu, J., Pinheiro, J., & Liu, Y. (2008) · Biometrical Journal · doi:10.1002/bimj.200810438 · Cited by 101
Self-citationHierarchical testing of multiple endpoints in group‐sequential trials
Glimm, E., Maurer, W., & Bretz, F. (2010) · Statistics in Medicine · doi:10.1002/sim.3748 · Cited by 89
Self-citationLiu, Z., & Lin, X. (2019) · Journal of the American Statistical Association · doi:10.1080/01621459.2018.1513363 · Cited by 36
The Trio Identity for Quasi-Monte Carlo Error
Hickernell, F. J. (2018) · Springer Proceedings in Mathematics & Statistics · doi:10.1007/978-3-319-91436-7_1 · Cited by 9
Metadata
- Authors
- Genz, A., & Bretz, F.
- Venue
- Journal of Computational and Graphical Statistics
- Publisher
- Informa UK Limited
- Published
- 2002-12
- Volume
- 11
- Issue
- 4
- Pages
- 950-971
- ISSN
- 1061-8600 (ISSN-L)
- 1537-2715
- OpenAlex
- W1969682141
- Cited by
- 394
- References
- 0
- FWCI
- 12.60
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
