Hazard Rate Estimation for Censored Data via Strong Representation of the Kaplan-Meier Estimator.

Abstract

This document studies the estimation of a hazard rate function based on censored data by the kernel smoothing method. Our technique is facilitated by a recent result of Lo and Singh (1984) which establishes a strong uniform approximation of the Kaplan-Meier estimator by an average of independent random variables. Pointwise and uniform strong consistency are derived, as well as the mean squared error expression and asymptotic normality, which is obtained using a more traditional method, as compared with the Hajek projection employed by Tanner and Wong (1983). (Author)

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1985
Accession Number
ADA172031

Entities

People

  • J. L. Wang
  • S. H. Lo
  • Y. P. Mack

Organizations

  • University of California, Davis

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Asymptotic Normality
  • California
  • Censorship
  • Consistency
  • Covariance
  • Data Science
  • Distribution Functions
  • Information Science
  • Normality
  • Observation
  • Probability
  • Random Variables
  • Security
  • Statistical Analysis
  • Statistics
  • Survival
  • Universities

Fields of Study

  • Mathematics

Readers

  • Statistical inference.