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dc.contributor.authorWang, Yen_NZ
dc.contributor.authorTaylor, SMen_NZ
dc.date.accessioned2017-08-02T00:11:51Z
dc.date.available2017-08-02T00:11:51Z
dc.date.copyright2013en_NZ
dc.identifier.citationStatistics and Computing, 23(6), 713-725.
dc.identifier.issn0960-3174en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/10711
dc.description.abstractWe propose a new algorithm for computingthe maximum likelihood estimate of a nonparametric survivalfunction for interval-censored data, by extending therecently-proposed constrained Newton method in a hierarchicalfashion. The new algorithm makes use of the fact thata mixture distribution can be recursively written as a mixtureof mixtures, and takes a divide-and-conquer approach tobreak down a large-scale constrained optimization probleminto many small-scale ones, which can be solved rapidly.During the course of optimization, the new algorithm, whichwe call the hierarchical constrained Newton method, can efficientlyreallocate the probability mass, both locally andglobally, among potential support intervals. Its convergenceis theoretically established based on an equilibrium analysis.Numerical study results suggest that the new algorithmis the best choice for data sets of any size and for solutionswith any number of support intervals.en_NZ
dc.publisherSpringer
dc.relation.urihttps://link.springer.com/article/10.1007%2Fs11222-012-9341-9
dc.rightsAuthors may self-archive the author’s accepted manuscript of their articles on their own websites. Authors may also deposit this version of the article in any repository, provided it is only made publicly available 12 months after official publication or later. He/ she may not use the publisher's version (the final article), which is posted on SpringerLink and other Springer websites, for the purpose of self-archiving or deposit. Furthermore, the author may only post his/her version provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website.
dc.subjectNonparametric maximum likelihooden_NZ
dc.subjectSurvival functionen_NZ
dc.subjectInterval censoringen_NZ
dc.subjectClinical trialen_NZ
dc.subjectConstrained Newton methoden_NZ
dc.subjectDisease-free survivalen_NZ
dc.titleEfficient Computation of Nonparametric Survival Functions Via a Hierarchical Mixture Formulationen_NZ
dc.typeJournal Article
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.1007/s11222-012-9341-9en_NZ
aut.relation.endpage725
aut.relation.issue6en_NZ
aut.relation.startpage713
aut.relation.volume23en_NZ
pubs.elements-id113922
aut.relation.journalStatistics and Computingen_NZ


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