An Analysis of Transformations

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1964
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Ranked #135 of 145 articles by views in Journal of the Royal Statistical Society Series B (Statistical Methodology)

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Abstract
Summary In the analysis of data it is often assumed that observations y 1, y 2, …, yn are independently normally distributed with constant variance and with expectations specified by a model linear in a set of parameters θ. In this paper we make the less restrictive assumption 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.
Reference Key
openalex_W129305155 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors George E. P. Box, David R. Cox
Journal Journal of the Royal Statistical Society Series B (Statistical Methodology)
Year 1964
DOI
10.1111/j.2517-6161.1964.tb00553.x
URL
https://doi.org/10.1111/j.2517-6161.1964.tb00553.x
Keywords Keywords not found

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