IDEAS home Printed from
   My bibliography  Save this article

Testing the homogeneity of inverse Gaussian scale-like parameters


  • Chang, Ming
  • You, Xuqun
  • Wen, Muqing


A test for the homogeneity of normal variances was proposed by Liu and Xu [Liu, X.H., Xu, X.Z., 2010. A generalized p-value approach for testing the homogeneity of variances. Statistics and Probability Letters 80, 1486–1491]. For testing the homogeneity of inverse Gaussian scale-like parameters, a parallel test is developed in this article. The proposed test is proved to have exact frequent property. The merits of the proposed method are numerically compared with the existing method with respect to their sizes and powers under different scenarios. The simulation results show that the proposed approach can perform hypothesis testing with satisfactory sizes and powers.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:stapro:v:82:y:2012:i:10:p:1755-1760
    DOI: 10.1016/j.spl.2012.05.013

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    1. Liu, Xuhua & Xu, Xingzhong, 2010. "A new generalized p-value approach for testing the homogeneity of variances," Statistics & Probability Letters, Elsevier, vol. 80(19-20), pages 1486-1491, October.
    2. Tian, Lili, 2006. "Testing equality of inverse Gaussian means under heterogeneity, based on generalized test variable," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1156-1162, November.
    3. Li, Xinmin, 2009. "A generalized p-value approach for comparing the means of several log-normal populations," Statistics & Probability Letters, Elsevier, vol. 79(11), pages 1404-1408, June.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Sadooghi-Alvandi, Soltan Mohammad & Malekzadeh, Ahad, 2013. "A note on testing homogeneity of the scale parameters of several inverse Gaussian distributions," Statistics & Probability Letters, Elsevier, vol. 83(8), pages 1844-1848.
    2. A. C. M. Wong, 2016. "Testing homogeneity of inverse Gaussian scale-like parameters: a saddlepoint approach," Statistical Papers, Springer, vol. 57(2), pages 319-327, April.


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:stapro:v:82:y:2012:i:10:p:1755-1760. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.