Weak Convergence of a Bayesian Nonparametric Estimator of the Survival Function under Progressive Censoring,

Abstract

A nonparametric Bayesian estimator F (circumflex) of the survival function F constructed from time-sequential progressively censored observations is found to subsume several estimators of F utilized in practice. Weak convergence of F (circumflex) is developed and the limiting process is found to coincide with that obtained when complete response profiles of the sample are available, leading to suitable application of F (circumflex) with consequent reductions in costs and time and without loss of asymptotic accuracy. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1982
Accession Number
ADA111858

Entities

People

  • Joseph C. Gardiner
  • V. Susarla

Organizations

  • Michigan State University

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Accuracy
  • Clinical Trials
  • Convergence
  • Data Science
  • Data Sets
  • Distribution Functions
  • Estimators
  • Gaussian Processes
  • Information Science
  • Michigan
  • Military Research
  • New York
  • Operations Research
  • Probability
  • Sequences
  • Survival
  • Weak Convergence

Fields of Study

  • Mathematics

Readers

  • Mathematical Modeling and Probability Theory.
  • Plasma Physics.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms