On the Asymptotic Properties of a Kernel-Type Quantile Estimator from Censored Samples.

Abstract

In reliability and medical studies, it is often of interest to estimate various quantiles of the unknown lifetime distribution. In particular, the median lifetime and extreme quantiles are of interest to the experimenter in such studies. In many life testing and medical follow-up experiments, however, arbritrarily right-censored data arise, and it is important to be able to estimate the quantiles of interest based on the censored data. For such data, some kernel-type quantile estimators are considered in this paper which give smoother estimates than the usual product-limit quantile function. Keywords: Random right-censorship; Kernel estimation; Product-limit quantile function; Asymptotic normality; and Mean-square convergence.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1985
Accession Number
ADA160302

Entities

People

  • K. F. Yu
  • William J. Padgett
  • Y. O. Lio

Organizations

  • University of South Carolina

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Air Force
  • Asymptotic Normality
  • Censorship
  • Convergence
  • Data Science
  • Distribution Functions
  • Information Science
  • Kernel Functions
  • Mathematics
  • Normality
  • Probability
  • Probability Distributions
  • Random Variables
  • South Carolina
  • Statistical Algorithms
  • Statistics
  • Theorems

Fields of Study

  • Mathematics

Readers

  • Statistical inference.