Estimation in Highly Skewed Data,

Abstract

The problem of inference for the mean of a highly asymmetric distribution is considered. Even with large sample sizes, usual asymptotics (i.e., normal theory) give poor answers, and standard modifications, such as higher moment correction factors, provide little help. We attempt to develop diagnostics to indicate when inferences are likely to be valid, and we examine the performance of several modifications to the standard procedure. The problem is illustrated with data from particle physics.

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1992
Accession Number
ADP007193

Entities

People

  • Shane P. Pederson

Organizations

  • Los Alamos National Laboratory

Tags

DTIC Thesaurus Topics

  • Computer Science
  • Data Science
  • Engineering
  • Information Science
  • Particle Physics
  • Particles
  • Physics
  • Standards
  • Statistics
  • Theoretical Computer Science

Fields of Study

  • Mathematics

Readers

  • Computational Modeling and Simulation
  • Statistical inference.

Technology Areas

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