Accelerated Rates Regression Models for Recurrent Failure Time Data
dc.contributor.author | Ghosh, Debashis | en_US |
dc.date.accessioned | 2006-09-11T18:13:33Z | |
dc.date.available | 2006-09-11T18:13:33Z | |
dc.date.issued | 2004-09 | en_US |
dc.identifier.citation | Ghosh, Debashis; (2004). "Accelerated Rates Regression Models for Recurrent Failure Time Data." Lifetime Data Analysis 10(3): 247-261. <http://hdl.handle.net/2027.42/46806> | en_US |
dc.identifier.issn | 1380-7870 | en_US |
dc.identifier.issn | 1572-9249 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/46806 | |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=15456106&dopt=citation | en_US |
dc.description.abstract | In this article, we formulate a semiparametric model for counting processes in which the effect of covariates is to transform the time scale for a baseline rate function. We assume an arbitrary dependence structure for the counting process and propose a class of estimating equations for the regression parameters. Asymptotic results for these estimators are derived. In addition, goodness of fit methods for assessing the adequacy of the accelerated rates model are proposed. The finite-sample behavior of the proposed methods is examined in simulation studies, and data from a chronic granulomatous disease study are used to illustrate the methodology. | en_US |
dc.format.extent | 177377 bytes | |
dc.format.extent | 3115 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Kluwer Academic Publishers; Springer Science+Business Media | en_US |
dc.subject.other | Statistics | en_US |
dc.subject.other | Statistics, General | en_US |
dc.subject.other | Statistics for Business/Economics/Mathematical Finance/Insurance | en_US |
dc.subject.other | Statistics for Life Sciences, Medicine, Health Sciences | en_US |
dc.subject.other | Quality Control, Reliability, Safety and Risk | en_US |
dc.subject.other | Operation Research/Decision Theory | en_US |
dc.subject.other | Counting Process | en_US |
dc.subject.other | Multiple Events | en_US |
dc.subject.other | Poisson Process | en_US |
dc.subject.other | Survival Data | en_US |
dc.title | Accelerated Rates Regression Models for Recurrent Failure Time Data | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Mathematics | en_US |
dc.subject.hlbsecondlevel | Statistics and Numeric Data | en_US |
dc.subject.hlbtoplevel | Social Sciences | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, MI, 48109-2029, USA | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.identifier.pmid | 15456106 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/46806/1/10985_2004_Article_5276745.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1023/B:LIDA.0000036391.87081.e3 | en_US |
dc.identifier.source | Lifetime Data Analysis | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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