Edgeworth Expansions in Statistics: A Brief Review.

Abstract

Asymptotic expansions for distribution functions are of great importance in statistical inference. The basic aspects and some recent developments regarding Edgeworth expansions and Cornish-Fisher expansions are discussed. In recent years, substantial progress has been made in obtaining Berry-Esseen bounds and Edgeworth expansions for test statistics such as linear rank statistics, U-statistics, linear (trimmed and untrimmed) combinations of order statistics. An account of these results is given in Section 4 followed by expansions for minimum contrast estimators and Fisher-consistent estimators.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1982
Accession Number
ADA117061

Entities

People

  • S. Panchapakesan
  • Shanti Gupta

Organizations

  • Purdue University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Applied Mathematics
  • Asymptotic Series
  • Data Science
  • Differential Equations
  • Distribution Functions
  • Information Science
  • New York
  • Normal Distribution
  • Order Statistics
  • Probability
  • Random Variables
  • Statistical Algorithms
  • Statistical Distributions
  • Statistical Inference
  • Statistics
  • Surveys
  • Theorems

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

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