Generalized modulus power transformations

Varghese George, Robert C. Elston

Research output: Contribution to journalArticle

34 Citations (Scopus)

Abstract

Several power transformations proposed in the past are examined to find out the type of distributions that they can normalize, and a general family of transformations, “the Generalized Modulus Power Transformation” (GEMPT), is proposed. The GEMPT will remove skewness and kurtosis and induce normality from a broad class of distributions, which we investigate, implying certain limitations for all power transformations. The use of GEMPT is illustrated and shown to lead to a better approximation to a normal distribution in an example in which the response is expected to follow a rectangular hyperbola.

Original languageEnglish (US)
Pages (from-to)2933-2952
Number of pages20
JournalCommunications in Statistics - Theory and Methods
Volume17
Issue number9
DOIs
StatePublished - Jan 1 1988

Fingerprint

Power Transformation
Modulus
Rectangular hyperbola
Normalize
Kurtosis
Skewness
Normality
Gaussian distribution
Approximation

Keywords

  • Michaelis-Menten equation
  • bimodality
  • kinky distributions
  • kurtosis
  • normality
  • skewness

ASJC Scopus subject areas

  • Statistics and Probability

Cite this

Generalized modulus power transformations. / George, Varghese; Elston, Robert C.

In: Communications in Statistics - Theory and Methods, Vol. 17, No. 9, 01.01.1988, p. 2933-2952.

Research output: Contribution to journalArticle

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