Quasi-Monte Carlo for highly structured generalised response models

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dc.contributor.author Kuo, FY
dc.contributor.author Dunsmuir, WT
dc.contributor.author Sloan, IH
dc.contributor.author Wand, M
dc.contributor.author Womersley, RS
dc.date.accessioned 2012-02-02T04:17:31Z
dc.date.issued 2008-01
dc.identifier.citation Methodology and Computing in Applied Probability, 2008, 10 (2), pp. 239 - 275
dc.identifier.issn 1387-5841
dc.identifier.other C1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/14509
dc.description.abstract Highly structured generalised response models, such as generalised linear mixed models and generalised linear models for time series regression, have become an indispensable vehicle for data analysis and inference in many areas of application. However, their use in practice is hindered by high-dimensional intractable integrals. Quasi-Monte Carlo (QMC) is a dynamic research area in the general problem of high-dimensional numerical integration, although its potential for statistical applications is yet to be fully explored. We survey recent research in QMC, particularly lattice rules, and report on its application to highly structured generalised response models. New challenges for QMC are identified and new methodologies are developed. QMC methods are seen to provide significant improvements compared with ordinary Monte Carlo methods.
dc.publisher Springer New York LLC
dc.relation.isbasedon 10.1007/s11009-007-9045-3
dc.title Quasi-Monte Carlo for highly structured generalised response models
dc.type Journal Article
dc.parent Methodology and Computing in Applied Probability
dc.journal.volume 2
dc.journal.volume 10
dc.journal.number 2 en_US
dc.publocation United States en_US
dc.publocation Valletta, Malta
dc.identifier.startpage 239 en_US
dc.identifier.endpage 275 en_US
dc.cauo.name SCI.Mathematical Sciences en_US
dc.conference Verified OK en_US
dc.conference Multimodal Corpora: Advances in Capturing, Coding and Analyzing Multimodality
dc.for 0104 Statistics
dc.personcode 110509
dc.percentage 100 en_US
dc.classification.name Statistics en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.date.activity 2010-05-17
dc.location.activity en_US
dc.location.activity Valletta, Malta
dc.description.keywords Generalised linear mixed models - High-dimensional integration - Lattice rules - Longitudinal data analysis - Maximum likelihood - Quasi-Monte Carlo - Semiparametric regression - Serial dependence - Time series regression
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Science
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
pubs.consider-herdc false
utslib.collection.history School of Mathematical Sciences (ID: 340)
utslib.collection.history Closed (ID: 3)

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