Strong representation of a generalized product-limit estimator for truncated and censored data with some applications

Iglesias-Pérez, María del Carmen; González–Manteiga, Wenceslao

doi:10.1080/10485259908832761

Cited by 52 publications

(37 citation statements)

References 12 publications

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“…Equation (5) is, in its turn, a consequence of Lemma 5 in Iglesias-Pérez and González-Manteiga (1999).…”

Section: Large Sample Resultsmentioning

confidence: 90%

“…3, the asymptotic properties of the estimator are investigated, including rates of convergence, limiting distribution, and efficiency results. In particular, we show that the length-bias assumption results in less variance in estimation, when compared to methods based on observed truncation times (Iglesias-Pérez and González-Manteiga 1999). In Sect.…”

Section: Introductionmentioning

confidence: 98%

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Nonparametric estimation of a conditional distribution from length-biased data

Uña‐Álvarez

Iglesias-Pérez

2008

Ann Inst Stat Math

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“…Equation (5) is, in its turn, a consequence of Lemma 5 in Iglesias-Pérez and González-Manteiga (1999).…”

Section: Large Sample Resultsmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 98%

Nonparametric estimation of a conditional distribution from length-biased data

Uña‐Álvarez

Iglesias-Pérez

2008

Ann Inst Stat Math

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“…The well-known product limit estimator introduced by Kaplan and Meier [21] enables nonparametric estimation of the unconditional survivor function. Dabrowska [22], Dabrowska [23], González Manteiga and Cadarso-Suarez [24] and Iglesias Pérez and González Manteiga [25] present generalizations of the product limit estimator by introducing kernel-based weights to estimate the conditional survivor function nonparametrically. In the light of counting process theory, McKeague and Utikal [26] propose a general counting process regression model for estimating conditional survivor functions, and Li and Doss [27] propose a class of estimators for the conditional survivor function based on a fully nonparametric model.…”

Section: Introductionmentioning

confidence: 99%

Conditional Transformation Models for Survivor Function Estimation

Möst

Hothorn²

2015

The International Journal of Biostatistics

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In survival analysis, the estimation of patient-specific survivor functions that are conditional on a set of patient characteristics is of special interest. In general, knowledge of the conditional survival probabilities of a patient at all relevant time points allows better assessment of the patient's risk than summary statistics, such as median survival time. Nevertheless, standard methods for analysing survival data seldom estimate the survivor function directly. Therefore, we propose the application of conditional transformation models (CTMs) for the estimation of the conditional distribution function of survival times given a set of patient characteristics. We used the inverse probability of censoring weighting approach to account for right-censored observations. Our proposed modelling approach allows the prediction of patientspecific survivor functions. In addition, CTMs constitute a flexible model class that is able to deal with proportional as well as non-proportional hazards. The well-known Cox model is included in the class of CTMs as a special case. We investigated the performance of CTMs in survival data analysis in a simulation that included proportional and non-proportional hazard settings and different scenarios of explanatory variables. Furthermore, we re-analysed the survival times of patients suffering from chronic myelogenous leukaemia and studied the impact of the proportional hazards assumption on previously published results.

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“…In this case we will assume that Y is independent of (T, C) conditionally on X. A general estimator in the single covariate context has been introduced by Iglesias-Pérez and González-Manteiga (1999):…”

Section: Introductionmentioning

confidence: 99%

Goodness-of-fit tests for conditional models under censoring and truncation

Cao¹,

González–Manteiga²

2008

Journal of Econometrics

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To cite this version:Ricardo Cao, Wenceslao González-Manteiga. Goodness-of-fit tests for conditional models under censoring and truncation.Journal of Econometrics, Elsevier, 2008, 143 (1) This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. A c c e p t e d m a n u s c r i p t ABSTRACTThe problem of specification tests for conditional models is studied when the data are subject to left truncation and right censoring. A general method is applied to derive tests for the polynomial regression, the proportional hazards, the additive risks and the proportional odds models. Bootstrap versions to approximate the critical values of the test are introduced and proved to work both from a theoretical viewpoint as well as in a small simulation study.JEL Classification: C12, C15, C24

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Strong representation of a generalized product-limit estimator for truncated and censored data with some applications

Cited by 52 publications

References 12 publications

Nonparametric estimation of a conditional distribution from length-biased data

Nonparametric estimation of a conditional distribution from length-biased data

Conditional Transformation Models for Survivor Function Estimation

Goodness-of-fit tests for conditional models under censoring and truncation

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