Asymptotic Theory for Nonparametric Confidence Intervals.

Abstract

In practice, nonparametric confidence intervals often have undesirable small sample asymmetry and converge characteristics. By applying Edgeworth expansion theory, we obtain asymptotic expansions for the errors associated with nonparametric confidence intervals. This analysis isolates the various elements that contribute to error. We then proceed to develop first and second order corrections to the standard nonparametric interval, which deal with asymmetry problems and coverage difficulties, respectively. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1982
Accession Number
ADA119196

Entities

People

  • Peter W. Glynn

Organizations

  • Stanford University

Tags

Communities of Interest

  • Air Platforms
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Asymptotic Series
  • Data Science
  • Errors
  • Estimators
  • Information Science
  • Monte Carlo Method
  • New York
  • Probability
  • Sampling
  • Sequences
  • Simulations
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Inference
  • Statistics
  • Stochastic Processes
  • United States

Fields of Study

  • Mathematics

Readers

  • Calculus or Mathematical Analysis
  • Regression Analysis.