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Sankhya B

, Volume 81, Issue 1, pp 60–74 | Cite as

On Testing the Inverse Gaussian Distribution Hypothesis

  • José A. VillaseñorEmail author
  • Elizabeth González-Estrada
  • Adrián Ochoa
Article

Abstract

The family of Inverse Gaussian (IG) distributions has applications in areas such as hydrology, lifetime testing, and reliability, among others. In this paper, a new characterization for this family of distributions is introduced and is used to propose a test of fit for the IG distribution hypothesis with unknown parameters. As a second test, observations are transformed to normal variables and then Shapiro-Wilk test is used to test for normality. Simulation results show that the proposed tests preserve the nominal test size and are competitive against some existing tests for the same problem. Three real datasets are used to illustrate the application of these tests.

Keywords and phrases

Anderson-Darling test Characterizations Convolution Data transformations Gamma distribution Goodness-of-fit test Shapiro-Wilk test 

AMS (2000) subject classification.

Primary 62F03 62H15 62H05; Secondary 62F40 62F05 62P30 

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Notes

Acknowledgements

The authors are grateful to the associate editor and two anonymous reviewers for their constructive comments and suggestions on the original version of this paper. The authors also thank Arturo Mancera-Rico for providing the dataset used in Example 2.

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Copyright information

© Indian Statistical Institute 2017

Authors and Affiliations

  1. 1.Colegio de PostgraduadosMéxico-TexcocoMéxico

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