The Large Sample Behavior of Transformations to Normality.

Abstract

We investigate the large sample behavior of both the classical and Bayesian procedures for selecting a transformation to normality. The study of the large sample behavior clearly reveals the role played by the assumptions leading to the Box and Cox procedures. Based on our large sample results, we introduce an information number approach for transforming a known distribution to near normality. This latter procedure provides bench marks for the maximum possible amount of improvement through power transformations. We illustrate our procedure with three examples. Finally, we generalize our procedure to random vectors and linear models situations. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1979
Accession Number
ADA080885

Entities

People

  • Fabian Hernandez
  • Richard A. Johnson

Organizations

  • University of Wisconsin Madison Department of Statistics

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Computing-Related Activities
  • Data Science
  • Information Science
  • Interdisciplinary Science
  • Mathematics
  • Normality
  • Plastic Explosives
  • Statistical Analysis
  • Statistics
  • Universities
  • Wisconsin

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.

Technology Areas

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