IDEAS home Printed from https://ideas.repec.org/a/spr/metrik/v77y2014i6p795-809.html
   My bibliography  Save this article

Testing equality of shape parameters in several inverse Gaussian populations

Author

Listed:
  • Cuizhen Niu
  • Xu Guo
  • Wangli Xu
  • Lixing Zhu

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. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Cuizhen Niu & Xu Guo & Wangli Xu & Lixing Zhu, 2014. "Testing equality of shape parameters in several inverse Gaussian populations," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(6), pages 795-809, August.
  • Handle: RePEc:spr:metrik:v:77:y:2014:i:6:p:795-809
    DOI: 10.1007/s00184-013-0465-5
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00184-013-0465-5
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s00184-013-0465-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

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

    References listed on IDEAS

    as
    1. Ye, Ren-Dao & Ma, Tie-Feng & Wang, Song-Gui, 2010. "Inferences on the common mean of several inverse Gaussian populations," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 906-915, April.
    2. Bai, Zhidong & Wang, Keyan & Wong, Wing-Keung, 2011. "The mean-variance ratio test--A complement to the coefficient of variation test and the Sharpe ratio test," Statistics & Probability Letters, Elsevier, vol. 81(8), pages 1078-1085, August.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Mohammad Reza Kazemi & Ali Akbar Jafari, 2019. "Inference about the shape parameters of several inverse Gaussian distributions: testing equality and confidence interval for a common value," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(5), pages 529-545, July.
    2. Amitava Mukherjee & Marco Marozzi, 2019. "A class of percentile modified Lepage-type tests," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(6), pages 657-689, August.
    3. Samadrita Bera & Nabakumar Jana, 2022. "On estimating common mean of several inverse Gaussian distributions," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(1), pages 115-139, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shi, Jian-Hong & Lv, Jiang-Long, 2012. "A new generalized p-value for testing equality of inverse Gaussian means under heterogeneity," Statistics & Probability Letters, Elsevier, vol. 82(1), pages 96-102.
    2. Mondal, Anjana & Kumar, Somesh, 2024. "Inference on order restricted means of inverse Gaussian populations under heteroscedasticity," Computational Statistics & Data Analysis, Elsevier, vol. 194(C).
    3. Xu Guo & Hecheng Wu & Gaorong Li & Qiuyue Li, 2017. "Inference for the common mean of several Birnbaum–Saunders populations," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(5), pages 941-954, April.
    4. Liu, Xuhua & Li, Na & Hu, Yuqin, 2015. "Combining inferences on the common mean of several inverse Gaussian distributions based on confidence distribution," Statistics & Probability Letters, Elsevier, vol. 105(C), pages 136-142.
    5. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2018. "Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology: Connections," JRFM, MDPI, vol. 11(1), pages 1-29, March.
    6. 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.
    7. 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.
    8. Zhihui Lv & Amanda M. Y. Chu & Wing Keung Wong & Thomas C. Chiang, 2021. "The maximum-return-and-minimum-volatility effect: evidence from choosing risky and riskless assets to form a portfolio," Risk Management, Palgrave Macmillan, vol. 23(1), pages 97-122, June.
    9. Kim-Hung Pho & Tuan-Kiet Tran & Thi Diem-Chinh Ho & Wing-Keung Wong, 2019. "Optimal Solution Techniques in Decision Sciences A Review," Advances in Decision Sciences, Asia University, Taiwan, vol. 23(1), pages 114-161, March.
    10. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2016. "Management Science, Economics and Finance: A Connection," Tinbergen Institute Discussion Papers 16-040/III, Tinbergen Institute.
    11. Nguyen Huu Hau & Tran Trung Tinh & Hoa Anh Tuong & Wing-Keung Wong, 2020. "Review of Matrix Theory with Applications in Education and Decision Sciences," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(1), pages 28-69, March.
    12. Leung, Pui-Lam & Ng, Hon-Yip & Wong, Wing-Keung, 2012. "An improved estimation to make Markowitz’s portfolio optimization theory users friendly and estimation accurate with application on the US stock market investment," European Journal of Operational Research, Elsevier, vol. 222(1), pages 85-95.
    13. Chang, C-L. & McAleer, M.J. & Wong, W.-K., 2018. "Management Information, Decision Sciences, and Financial Economics : a connection," Econometric Institute Research Papers 2018-004/III, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    14. Tiefeng Ma & Shuangzhe Liu & S. Ahmed, 2014. "Shrinkage estimation for the mean of the inverse Gaussian population," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(6), pages 733-752, August.
    15. Zura Kakushadze & Willie Yu, 2017. "Notes on Fano Ratio and Portfolio Optimization," Papers 1711.10640, arXiv.org, revised Apr 2018.
    16. 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.
    17. Park, Junyong & Park, DoHwan, 2012. "Testing the equality of a large number of normal population means," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1131-1149.
    18. Kim-Hung Pho & Thi Diem-Chinh Ho & Tuan-Kiet Tran & Wing-Keung Wong, 2019. "Moment Generating Function, Expectation And Variance Of Ubiquitous Distributions With Applications In Decision Sciences: A Review," Advances in Decision Sciences, Asia University, Taiwan, vol. 23(2), pages 65-150, June.
    19. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2018. "Decision Sciences, Economics, Finance, Business, Computing, and Big Data: Connections," Documentos de Trabajo del ICAE 2018-09, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    20. Li, Xinmin & Tian, Lili & Wang, Juan & Muindi, Josephia R., 2012. "Comparison of quantiles for several normal populations," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 2129-2138.

    Corrections

    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:spr:metrik:v:77:y:2014:i:6:p:795-809. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.