AN ANALYSIS OF TRANSFORMATIONS.

Abstract

In the analysis of data it is often assumed that observations y1, y2, . . . , y sub n are independently normally distributed with constant variance and with expectations specified by a model linear in a set of parameters theta. In this paper the less restrictive assumption is made that such a normal, homoscedastic, linear model is appropriate after some suitable transformation has been applied to the y's. Inferences about the transformation and about the parameters of the linear model are made by computing the likelihood function and the relevant posterior distribution. The contributions of normality, homoscedasticity and additivity to the transformation are separated. The relation of the present methods to earlier procedures for finding transformations is discussed. The methods are illustrated with examples. (Author)

Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1964
Accession Number
AD0604262

Entities

People

  • D. R. Cox
  • George E. P. Box

Organizations

  • University of Wisconsin–Madison

Tags

DTIC Thesaurus Topics

  • Computational Processes
  • Computing-Related Activities
  • Data Analysis
  • Data Mining
  • Data Science
  • Information Science
  • Normality
  • Observation

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference