The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Transformation of Statistical Variables

  • D. R. Cox
Reference work entry
DOI: https://doi.org/10.1057/978-1-349-95189-5_1826

Abstract

Transformations of many kinds are used in statistical method and theory including simple changes of unit of measurement to facilitate computation or understanding, and the linear transformations underlying the application and theory of multiple regression and the techniques of classical multivariate analysis. Nevertheless the word transformation in a statistical context normally brings to mind a non-linear transformation (to logs, square roots, etc.) of basic observations done with the objective of simplifying analysis and interpretation. The present entry focuses on that aspect.

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Bibliography

  1. Bartlett (1943) gives an excellent account of the early work; Box and Cox (1964) discuss the estimation of transformations via the likelihood and Bayesian methods. Butter and Verbon (1982) describe economic applications in some depth. Bickel and Doksum (1981) and Box and Cox (1982) give opposing views of estimation following a transformation.Google Scholar
  2. Bartlett, M.S. 1947. The use of transformations. Biometrics 3: 39–52.CrossRefGoogle Scholar
  3. Bickel, P.J., and K.A. Doksum. 1981. An analysis of transformations revisited. Journal of the American Statistical Association 76: 296–311.CrossRefGoogle Scholar
  4. Box, G.E.P., and D.R. Cox. 1964. An analysis of transformations. Journal of the Royal Statistical Society, Series B 26: 211–243.Google Scholar
  5. Box, G.E.P., and D.R. Cox. 1982. An analysis of transformations revisited, rebutted. Journal of the American Statistical Association 77: 209–210.CrossRefGoogle Scholar
  6. den Butter, F.A.G., and H.A.A. Verbon. 1982. The specification problem in regression analysis. International Statistical Review 50: 267–283.CrossRefGoogle Scholar

Copyright information

© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • D. R. Cox
    • 1
  1. 1.