On Density Estimation from Censored Data by Penalized Likelihood Methods.

Abstract

Estimators for the probability density function, cumulative distribution function, and hazard function are proposed in the random censorship setting. The estimators are derived from the Kaplan-Meier product limit estimator by maximum penalized likelihood methods. The authors establish the existence and uniqueness of the estimates, which are exponential splines with knots at the uncensored observations, and provide an efficient algorithm for their numerical evaluation. They prove the consistency, in probability and almost surely, of the density estimates in the Hellinger distance, the L sub p norms for p =1, 2, infinity, and the Sobolev norm. The corresponding hazard rate estimator converges uniformly on bounded intervals. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1985
Accession Number
ADA163388

Entities

People

  • John C. Wierman
  • V. K. Klonias

Organizations

  • Johns Hopkins University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Censorship
  • Computer Science
  • Consistency
  • Data Analysis
  • Data Science
  • Distribution Functions
  • Estimators
  • Information Science
  • Intervals
  • Observation
  • Probability
  • Probability Density Functions
  • Random Variables
  • Statistical Algorithms
  • Statistics
  • Test And Evaluation

Fields of Study

  • Mathematics

Readers

  • Approximation Theory.
  • Statistical inference.