Symmetrizing and Variance Stabilizing Transformations of Sample Coefficient of Variation from Inverse Gaussian Distribution

Yogendra P. Chaubey (), Murari Singh () and Debaraj Sen ()
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Yogendra P. Chaubey: Department of Mathematics and Statistics, Concordia University
Murari Singh: International Center for Agricultural Research in the Dry Areas
Debaraj Sen: Department of Mathematics and Statistics, Concordia University

Sankhya B: The Indian Journal of Statistics, 2017, vol. 79, issue 2, No 3, 217-246

Abstract: Abstract Coefficient of variation (CV) plays an important role in statistical practice; however, its sampling distribution may not be easy to compute. In this paper, the distributional properties of the sample CV from an inverse Gaussian distribution are investigated through transformations. Specifically, the symmetrizing transformation as outlined in Chaubey and Mudholkar (1983), that requires numerical techniques, is contrasted with the explicitly available variance stabilizing transformation (VST). The symmetrizing transformation scores very high as compared to the VST, especially in a power family. The usefulness of the resulting approximation is illustrated through a numerical example.

Keywords: Coefficient of variation; Inverse Gaussian distribution; Symmetrizing transformation; Variance stabilizing transformation; Primary 62E17; Secondary 62E30; 62E15; 62F03; 62E20; 62E25 (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s13571-017-0136-z

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