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A Directional Approach for Testing Homogeneity of Inverse Gaussian Scale-Like Parameters

Author

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  • Wong ACM

    (Department of Mathematics and Statistics, York University, Canada)

  • Zhang S

    (Department of Statistics and Actuarial Science, University of Waterloo, Canada)

Abstract

In recent years, likelihood-based higher order asymptotic methods have been developed to obtain inference for a scalar parameter of interest. In this paper, a directional test for a vector parameter of interest from a natural exponential family model is derived based on the likelihood-based higher order asymptotic methods. The proposed method is then applied to obtain p-value for testing homogeneity of inverse Gaussian scale-like parameters. Example and simulation results illustrate that the proposed method works almost perfectly

Suggested Citation

  • Wong ACM & Zhang S, 2017. "A Directional Approach for Testing Homogeneity of Inverse Gaussian Scale-Like Parameters," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 3(2), pages 34-39, September.
    • Handle: RePEc:adp:jbboaj:v:3:y:2017:i:2:p:34-39
    DOI: 10.19080/BBOAJ.2017.03.555608
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    References listed on IDEAS

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    1. Chang, Ming & You, Xuqun & Wen, Muqing, 2012. "Testing the homogeneity of inverse Gaussian scale-like parameters," Statistics & Probability Letters, Elsevier, vol. 82(10), pages 1755-1760.
    2. A. C. Davison & D. A. S. Fraser & N. Reid & N. Sartori, 2014. "Accurate Directional Inference for Vector Parameters in Linear Exponential Families," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 302-314, March.
    3. Ib M. Skovgaard, 2001. "Likelihood Asymptotics," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 28(1), pages 3-32, March.
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    Cited by:

    1. Ali Akbar Jafari & Javad Shaabani, 2020. "Comparing scale parameters in several gamma distributions with known shapes," Computational Statistics, Springer, vol. 35(4), pages 1927-1950, December.

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