Testing Transformations to Achieve Approximate Normality.

Abstract

We propose a competitor to likelihood and significance methods for power transformations to achieve approximate normality in a linear model. The new method is shown in theory and a Monte-Carlo experiment to produce more robust inferences than the likelihood method and considerably more powerful (although possibly slightly less robust) inferences than the significance method. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1978
Accession Number
ADA072596

Entities

People

  • Raymond J. Carroll

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Data Science
  • Distribution Functions
  • Information Science
  • Intervals
  • New York
  • Normal Distribution
  • Normality
  • North Carolina
  • Observation
  • Scientific Research
  • Standards
  • Statistical Analysis
  • Statistics
  • Universities

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks