Testing equality of shape parameters in several inverse Gaussian populations

Cuizhen Niu, Xu Guo, Wangli Xu, Lixing ZHU*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Due to the strikingly resemblance to the normal theory and inference methods, the inverse Gaussian (IG) distribution is commonly applied to model positive and right-skewed data. As the shape parameter in the IG distribution is greatly related to other important quantities such as the mean, skewness, kurtosis and the coefficient of variation, it plays an important role in distribution theory. This paper focuses on testing the equality of shape parameters in several inverse Gaussian distributions. Three tests are suggested: the exact generalized inference-based test, the asymptotic test and a test that is based on parametric bootstrap approximation. Simulation studies are undertaken to examine the performances of the these methods, and three real data examples are analyzed for illustration.

Original languageEnglish
Pages (from-to)795-809
Number of pages15
JournalMetrika
Volume77
Issue number6
DOIs
Publication statusPublished - Aug 2014

Scopus Subject Areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • Asymptotic test
  • Generalized inference
  • Inverse Gaussian distribution
  • Parametric bootstrap
  • Shape parameter

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