A Profile Conditional Likelihood Approach for the Semiparametric Transformation Regression Model with Missing Covariates
dc.contributor.author | Chen, Hua Yun | en_US |
dc.contributor.author | Little, Roderick J. A. | en_US |
dc.date.accessioned | 2006-09-11T18:16:49Z | |
dc.date.available | 2006-09-11T18:16:49Z | |
dc.date.issued | 2001-09 | en_US |
dc.identifier.citation | Chen, Hua Yun; Little, Roderick J.; (2001). "A Profile Conditional Likelihood Approach for the Semiparametric Transformation Regression Model with Missing Covariates." Lifetime Data Analysis 7(3): 207-224. <http://hdl.handle.net/2027.42/46850> | en_US |
dc.identifier.issn | 1380-7870 | en_US |
dc.identifier.issn | 1572-9249 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/46850 | |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=11677827&dopt=citation | en_US |
dc.description.abstract | We propose a profile conditional likelihood approach to handle missing covariates in the general semiparametric transformation regression model. The method estimates the marginal survival function by the Kaplan-Meier estimator, and then estimates the parameters of the survival model and the covariate distribution from a conditional likelihood, substituting the Kaplan-Meier estimator for the marginal survival function in the conditional likelihood. This method is simpler than full maximum likelihood approaches, and yields consistent and asymptotically normally distributed estimator of the regression parameter when censoring is independent of the covariates. The estimator demonstrates very high relative efficiency in simulations. When compared with complete-case analysis, the proposed estimator can be more efficient when the missing data are missing completely at random and can correct bias when the missing data are missing at random. The potential application of the proposed method to the generalized probit model with missing continuous covariates is also outlined. | en_US |
dc.format.extent | 145024 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 | Cox Model | en_US |
dc.subject.other | Gamma Odds Model | en_US |
dc.subject.other | Missing Pattern | en_US |
dc.title | A Profile Conditional Likelihood Approach for the Semiparametric Transformation Regression Model with Missing Covariates | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Statistics and Numeric Data | en_US |
dc.subject.hlbsecondlevel | Mathematics | 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, School of Public Health, University of Michigan, Ann Arbor, MI, 48109 | en_US |
dc.contributor.affiliationother | Division of Epidemiology and Biostatistics, School of Public Health, UIC 2121 West Taylor Street, Chicago, IL, 60612 | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.identifier.pmid | 11677827 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/46850/1/10985_2004_Article_354305.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1023/A:1011662322979 | en_US |
dc.identifier.source | Lifetime Data Analysis | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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