Risk-Efficient Estimation of the Mean Exponential Survival Time under Random Censoring,

Abstract

The paper proposes a sequential estimator theta of the parameter theta of an exponential distribution when the data is censored. Without any further conditions, it is shown that theta is asymptotically risk efficient when the loss is measured by the squared error loss of estimation of theta plus a linear cost function of the number of observations. In addition, it is shown that theta is asymptotically normal as the cost per observation to zero. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1983
Accession Number
ADA123913

Entities

People

  • Joseph C. Gardiner
  • V. Susarla

Organizations

  • Michigan State University

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Asymptotic Normality
  • Clinical Trials
  • Data Science
  • Estimators
  • Inequalities
  • Information Science
  • Life Tests
  • Michigan
  • Military Research
  • New York
  • Observation
  • Probability
  • Random Variables
  • Statistical Samples
  • Statistics
  • Survival
  • Theorems

Fields of Study

  • Mathematics

Readers

  • Statistical inference.