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July 26, 2026The American Statistician

A New Nonparametric Empirical Likelihood Estimator for Quantile Regression under Right Censoring

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Authors

MTMadiha Tour

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Overview

Randomized trial evaluates a new estimator for conditional quantile functions, suggesting robust inference despite right censoring.

Key Points

  • This research aims to develop a nonparametric empirical likelihood method to estimate conditional quantile functions under right censoring.
  • Introduces a new empirical likelihood method combined with inverse probability of censoring weighting and kernel smoothing.
  • Establishes theoretical properties such as consistency and asymptotic normality.
  • Conducts simulation studies to assess the finite-sample performance of the proposed estimator.
  • The proposed method results in asymptotically valid confidence intervals without explicit variance estimation.
  • Simulation studies demonstrate robust performance under various scenarios.
  • Application to real survival data shows practical utility and effectiveness of the method.

Cite This Study

Madiha Tour (2026) studied this question.

synapsesocial.com/papers/6a65a84ed3aea3239cd78c24https://doi.org/10.1080/00031305.2026.2709492
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Also Consider

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  1. 1New Non-Parametric Estimators of the Cumulative Distribution Function under Left-Censoring2026
  2. 2Two-sample empirical likelihood method for right censored data2025
  3. 3Quantile regression and smoothed empirical likelihood for non-ignorable missing data based on semi-parametric response models2024
  4. 4Quantile regression for censored data with a cure fraction2026
  5. 5Empirical Likelihood for Composite Quantile Regression Models with Missing Response Data2024