Testing equality of shape parameters in several inverse Gaussian populations

Cuizhen Niu, Xu Guo, Wangli Xu, Lixing Zhu

Research output: Contribution to journalArticle

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 (US)
Pages (from-to)795-809
Number of pages15
JournalMetrika
Volume77
Issue number6
DOIs
StatePublished - Jan 1 2014

Fingerprint

Inverse Gaussian Distribution
Inverse Gaussian
Shape Parameter
Equality
Testing
Asymptotic Test
Parametric Bootstrap
Distribution Theory
Coefficient of variation
Kurtosis
Skewness
Simulation Study
Approximation
Inverse Gaussian distribution
Model
Inference

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Niu, Cuizhen ; Guo, Xu ; Xu, Wangli ; Zhu, Lixing. / Testing equality of shape parameters in several inverse Gaussian populations. In: Metrika. 2014 ; Vol. 77, No. 6. pp. 795-809.
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Testing equality of shape parameters in several inverse Gaussian populations. / Niu, Cuizhen; Guo, Xu; Xu, Wangli; Zhu, Lixing.

In: Metrika, Vol. 77, No. 6, 01.01.2014, p. 795-809.

Research output: Contribution to journalArticle

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