Nonparametric estimation of a conditional distribution from length-biased data

Uña‐Álvarez, Jacobo de; Iglesias-Pérez, María del Carmen

doi:10.1007/s10463-008-0178-0

Cited by 11 publications

(9 citation statements)

References 25 publications

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“…We also impose a usual assumption that left truncation precedes right censoring with probability one, i.e., Pr(T ≤ C) = 1. This assumption includes Type I censoring of the form C = T + τ for some τ > 0, which often occurs in left-truncated data (Uña-Álvarez 2010).…”

Section: Proposed Methodologymentioning

confidence: 99%

Semiparametric inference for an accelerated failure time model with dependent truncation

Emura

Wang

2015

Ann Inst Stat Math

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Truncated data are commonly seen in studies of biomedicine, epidemiology, astronomy and econometrics. Existing regression methods for analyzing left-truncated and right-censored data have been developed under the assumption that the lifetime variable of interest is independent of both truncation and censoring variables. In this article, we propose a semiparametric accelerated failure time model that incorporates both covariates and the truncation variable as regressors. The proposed model utilizes the truncation information in statistical modeling and hence allows for dependent truncation. For estimation, we develop a set of estimating equations constructed from the log-rank and quasi-independence test statistics. We show that the resulting estimators are consistent and asymptotically normal. We also propose an explicit formula for variance estimation based on a kernel method. Finite-sample performances of the estimators are studied by simulations. The proposed methodology is applied to analyze a real data for illustration.

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Section: Proposed Methodologymentioning

confidence: 99%

Semiparametric inference for an accelerated failure time model with dependent truncation

Emura

Wang

2015

Ann Inst Stat Math

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“…, obtained by extending the conditional estimation methods introduced in de Uña-Alvarez and Iglesias-Perez (2008). Consequently, the estimator of the error distribution iŝ…”

Section: Description Of the Methodsmentioning

confidence: 99%

Computational Treatment of the Error Distribution in Nonparametric Regression with Right-Censored and Selection-Biased Data

Laurent

Heuchenne

2010

Proceedings of COMPSTAT'2010

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Abstract. Consider the regression modelare unknown smooth functions and the error ε (with unknown distribution) is independent of X. The pair (X, Y ) is subject to parametric selection bias and the response to right censoring. We construct a new estimator for the cumulative distribution function of the error ε, and develop a bootstrap technique to select the smoothing parameter involved in the procedure. The estimator is studied via extended simulations and applied to real unemployment data.

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“…Limitation in the following‐up period results in right‐censoring too; specifically, 56 per cent of the unemployment times are right censored. See de Uña‐Álvarez & Iglesias‐Pérez () for further description of this data set.…”

Section: Real Data Applicationmentioning

confidence: 99%

“…One interesting question is to relate the expected unemployment time with the age when entering the unemployment stock ( X ); previous results (e.g. de Uña‐Álvarez & Iglesias‐Pérez, ) suggest that relatively young women leave unemployment at shorter times, and this will be further explored in this paper from the mean regression viewpoint. As an additional complication, this data set includes several waves of inquires and, therefore, dependence among the unemployment times within each cluster (wave) is expected.…”

Section: Introductionmentioning

confidence: 99%

A Central Limit Theorem in Non‐parametric Regression with Truncated, Censored and Dependent Data

Liang

Uña‐Álvarez

Iglesias-Pérez

2014

Scandinavian J Statistics

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On the basis of the idea of the Nadaraya–Watson (NW) kernel smoother and the technique of the local linear (LL) smoother, we construct the NW and LL estimators of conditional mean functions and their derivatives for a left‐truncated and right‐censored model. The target function includes the regression function, the conditional moment and the conditional distribution function as special cases. It is assumed that the lifetime observations with covariates form a stationary α‐mixing sequence. Asymptotic normality of the estimators is established. Finite sample behaviour of the estimators is investigated via simulations. A real data illustration is included too.

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Nonparametric estimation of a conditional distribution from length-biased data

Abstract: Cross-sectional sampling, Left-truncation, Regression, Unemployment duration,

Cited by 11 publications

References 25 publications

Semiparametric inference for an accelerated failure time model with dependent truncation

Semiparametric inference for an accelerated failure time model with dependent truncation

Computational Treatment of the Error Distribution in Nonparametric Regression with Right-Censored and Selection-Biased Data

A Central Limit Theorem in Non‐parametric Regression with Truncated, Censored and Dependent Data

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