Further details on inference under right censoring for transformation models with a change-point based on a covariate threshold

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dc.creatorKosorok, Michael R.
dc.creatorSong, Rui
dc.date2006-04-03
dc.date.accessioned2026-07-07T08:07:42Z
dc.date.available2026-07-07T08:07:42Z
dc.descriptionWe consider linear transformation models applied to right censored survival data with a change-point based on a covariate threshold. We establish consistency and weak convergence of the nonparametric maximum lieklihood estimators. The change-point parameter is shown to be $n$-consistent, while the remaining parameters are shown to have the expected root-$n$ consistency. We show that the procedure is adaptive in the sense that the non-threshold parameters are estimable with the same precision as if the true threshold value were known. We also develop Monte-Carlo methods of inference for model parameters and score tests for the existence of a change-point. A key difficulty here is that some of the model parameters are not identifiable under the null hypothesis of no change-point. Simulation students establish the validity of the proposed score tests for finite sample sizes.
dc.descriptionUniversity of Wisconsin-Madison Department of Biostatistics and Medical Informatics Technical Report
dc.identifierhttps://arxiv.org/abs/math/0604043
dc.identifierhttp://arxiv.org/abs/math/0604043
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/131018
dc.subjectStatistics Theory
dc.subject62N01; 62F05; 62G20; 62G10
dc.titleFurther details on inference under right censoring for transformation models with a change-point based on a covariate threshold
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