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February 2021 Efron–Petrosian integrals for doubly truncated data with covariates: An asymptotic analysis
Jacobo de Uña-Álvarez, Ingrid Van Keilegom
Bernoulli 27(1): 249-273 (February 2021). DOI: 10.3150/20-BEJ1236

Abstract

In survival analysis, epidemiology and related fields there exists an increasing interest in statistical methods for doubly truncated data. Double truncation appears with interval sampling and other sampling schemes, and refers to situations in which the target variable is subject to two (left and right) random observation limits. Doubly truncated data require specific corrections for the observational bias, and this affects a variety of settings including the estimation of marginal and multivariate distributions, regression problems, and multi-state models. In this work multivariate Efron–Petrosian integrals for doubly truncated data are introduced. These integrals naturally arise when the goal is the estimation of the mean of a general transformation which involves the doubly truncated variable and covariates. An asymptotic representation of the Efron–Petrosian integrals as a sum of i.i.d. terms is derived and, from this, consistency and distributional convergence are established. As a by-product, uniform i.i.d. representations for the marginal nonparametric maximum likelihood estimator and its corresponding weighting process are provided. Applications to correlation analysis, regression, and competing risks models are presented. A simulation study is reported too.

Citation

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Jacobo de Uña-Álvarez. Ingrid Van Keilegom. "Efron–Petrosian integrals for doubly truncated data with covariates: An asymptotic analysis." Bernoulli 27 (1) 249 - 273, February 2021. https://doi.org/10.3150/20-BEJ1236

Information

Received: 1 November 2019; Revised: 1 May 2020; Published: February 2021
First available in Project Euclid: 20 November 2020

zbMATH: 07282850
MathSciNet: MR4177369
Digital Object Identifier: 10.3150/20-BEJ1236

Rights: Copyright © 2021 Bernoulli Society for Mathematical Statistics and Probability

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Vol.27 • No. 1 • February 2021
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