IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v78y2008i8p963-969.html
   My bibliography  Save this article

A new generalized p-value for ANOVA under heteroscedasticity

Author

Listed:
  • Xu, Li-Wen
  • Wang, Song-Gui

Abstract

For the problem of comparing the means of k populations with unequal population variances, a new generalized test variable is defined and the generalized p-value based on this generalized test variable is given. It is shown that the proposed generalized p-value is invariant under the group of scale transformations. Numerical results show that the proposed generalized p-value test performs better than a generalized F-test.

Suggested Citation

  • Xu, Li-Wen & Wang, Song-Gui, 2008. "A new generalized p-value for ANOVA under heteroscedasticity," Statistics & Probability Letters, Elsevier, vol. 78(8), pages 963-969, June.
    • Handle: RePEc:eee:stapro:v:78:y:2008:i:8:p:963-969
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(07)00348-3
    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

    as
    1. Gamage, Jinadasa & Mathew, Thomas & Weerahandi, Samaradasa, 2004. "Generalized p-values and generalized confidence regions for the multivariate Behrens-Fisher problem and MANOVA," Journal of Multivariate Analysis, Elsevier, vol. 88(1), pages 177-189, January.
    2. Weerahandi, Samaradasa, 1987. "Testing Regression Equality with Unequal Variances," Econometrica, Econometric Society, vol. 55(5), pages 1211-1215, September.
    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. H. V. Kulkarni & S. M. Patil, 2021. "Uniformly implementable small sample integrated likelihood ratio test for one-way and two-way ANOVA under heteroscedasticity and normality," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(2), pages 273-305, June.

    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. MacKinnon, J G, 1989. "Heteroskedasticity-Robust Tests for Structural Change," Empirical Economics, Springer, vol. 14(2), pages 77-92.
    2. S. H. Lin & R. S. Wang, 2009. "Inferences on a linear combination of K multivariate normal mean vectors," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(4), pages 415-428.
    3. Xu, Li-Wen, 2015. "Parametric bootstrap approaches for two-way MANOVA with unequal cell sizes and unequal cell covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 291-303.
    4. Su, Haiyan & Liang, Hua, 2010. "An empirical likelihood-based method for comparison of treatment effects--Test of equality of coefficients in linear models," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1079-1088, April.
    5. Scholz, Achim & Neumeyer, Natalie & Munk, Axel, 2004. "Nonparametric Analysis of Covariance : the Case of Inhomogeneous and Heteroscedastic Noise," Technical Reports 2004,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    6. Tang, Shijie & Tsui, Kam-Wah, 2007. "Distributional properties for the generalized p-value for the Behrens-Fisher problem," Statistics & Probability Letters, Elsevier, vol. 77(1), pages 1-8, January.
    7. Jin-Ting Zhang & Xuefeng Liu, 2013. "A modified Bartlett test for heteroscedastic one-way MANOVA," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(1), pages 135-152, January.
    8. S.H. Lin, 2014. "Comparing the mean vectors of two independent multivariate log-normal distributions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(2), pages 259-274, February.
    9. Lauren Bin Dong, 2004. "Testing for structural Change in Regression: An Empirical Likelihood Ratio Approach," Econometrics Working Papers 0405, Department of Economics, University of Victoria.
    10. Lai, Chin-Ying & Tian, Lili & Schisterman, Enrique F., 2012. "Exact confidence interval estimation for the Youden index and its corresponding optimal cut-point," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1103-1114.
    11. Rendao Ye & Tiefeng Ma & Songgui Wang, 2011. "Generalized confidence intervals for the process capability indices in general random effect model with balanced data," Statistical Papers, Springer, vol. 52(1), pages 153-169, February.
    12. Sumith Gunasekera, 2015. "Generalized inferences of $$R$$ R = $$\Pr (X>Y)$$ Pr ( X > Y ) for Pareto distribution," Statistical Papers, Springer, vol. 56(2), pages 333-351, May.
    13. Roy, Anindya & Bose, Arup, 2009. "Coverage of generalized confidence intervals," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1384-1397, August.
    14. Mar Fenoy & Pilar Ibarrola & Juan B. Seoane-Sepúlveda, 2019. "Generalized p value for multivariate Gaussian stochastic processes in continuous time," Statistical Papers, Springer, vol. 60(6), pages 2013-2030, December.
    15. Ho, Yu-Yun & Weerahandi, Sam, 2007. "Analysis of repeated measures under unequal variances," Journal of Multivariate Analysis, Elsevier, vol. 98(3), pages 493-504, March.
    16. Lajos Horváth & Gregory Rice, 2015. "Testing Equality Of Means When The Observations Are From Functional Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(1), pages 84-108, January.
    17. Gebrenegus Ghilagaber, 2004. "Another Look at Chow's Test for the Equality of Two Heteroscedastic Regression Models," Quality & Quantity: International Journal of Methodology, Springer, vol. 38(1), pages 81-93, February.
    18. Lauren Bin Dong, 2004. "The Behrens-Fisher Problem: An Empirical Likelihood Ratio Approach," Econometrics Working Papers 0404, Department of Economics, University of Victoria.
    19. Bebu, Ionut & Luta, George & Mathew, Thomas & Kennedy, Paul A. & Agan, Brian K., 2016. "Parametric cost-effectiveness inference with skewed data," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 210-220.
    20. Masafumi Akahira, 2002. "Confidence intervals for the difference of means: application to the Behrens-Fisher type problem," Statistical Papers, Springer, vol. 43(2), pages 273-284, April.

    More about this item

    Statistics

    Access and download statistics

    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:eee:stapro:v:78:y:2008:i:8:p:963-969. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.