IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v40y2025i1d10.1007_s00180-024-01481-6.html
   My bibliography  Save this article

Simultaneous confidence bands for multiple comparisons of several percentile lines

Author

Listed:
  • Sanyu Zhou

    (Shanghai University of Finance and Economics)

  • Yu Zhang

    (Shenzhen University)

Abstract

In practice, it is often necessary to compare several percentile lines. To that end, a set of simultaneous confidence bands has been constructed. The contributions of this research are as follows: (1) the proposed bands are constructed and used to multiple comparisons of several percentile lines for the first time; (2) they allow to draw various comparisons: pairwise, successive and many-to-one; and (3) the comparisons can be drawn on any intervals of interest, and provide more information on both the magnitude and the direction of difference. In addition, practical applications are presented.

Suggested Citation

  • Sanyu Zhou & Yu Zhang, 2025. "Simultaneous confidence bands for multiple comparisons of several percentile lines," Computational Statistics, Springer, vol. 40(1), pages 111-123, January.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:1:d:10.1007_s00180-024-01481-6
    DOI: 10.1007/s00180-024-01481-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-024-01481-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-024-01481-6?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. Liu W. & Jamshidian M. & Zhang Y., 2004. "Multiple Comparison of Several Linear Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 395-403, January.
    2. Lu, Xiaolei & Kuriki, Satoshi, 2017. "Simultaneous confidence bands for contrasts between several nonlinear regression curves," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 83-104.
    3. Sanyu Zhou, 2017. "One-sided hyperbolic simultaneous confidence bands for multiple and polynomial regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(2), pages 187-200, February.
    4. Zhou, S. & Yao, K. & Liu, W. & Bretz, F., 2022. "Construction of simultaneous confidence bands using conditional Monte Carlo," Statistics & Probability Letters, Elsevier, vol. 182(C).
    Full references (including those not matched with items on IDEAS)

    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. Sanyu Zhou & Defa Wang & Jingjing Zhu, 2020. "Construction of simultaneous confidence bands for a percentile hyper-plane with predictor variables constrained in an ellipsoidal region," Statistical Papers, Springer, vol. 61(3), pages 1335-1346, June.
    2. Chang-Yu Liu & Xin Liu & Rong-Xian Yue, 2024. "Locally optimal designs for comparing curves in generalized linear models," Statistical Papers, Springer, vol. 65(5), pages 3181-3201, July.
    3. 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.
    4. Sanyu Zhou, 2017. "One-sided hyperbolic simultaneous confidence bands for multiple and polynomial regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(2), pages 187-200, February.
    5. Lu, Xiaolei & Kuriki, Satoshi, 2017. "Simultaneous confidence bands for contrasts between several nonlinear regression curves," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 83-104.
    6. Jamshidian, Mortaza & Jennrich, Robert I. & Liu, Wei, 2007. "A study of partial F tests for multiple linear regression models," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6269-6284, August.
    7. Sanyu Zhou, 2018. "An exact method for the multiple comparison of several polynomial regression models with applications in dose-response study," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(3), pages 413-429, July.
    8. Frank Schaarschmidt, 2017. "Multiple treatment comparisons in analysis of covariance with interaction," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(4), pages 609-628, November.
    9. Christopher Withers & Saralees Nadarajah, 2012. "Maximum modulus confidence bands," Statistical Papers, Springer, vol. 53(4), pages 811-819, November.
    10. Sophia Rosen & Ori Davidov, 2017. "Ordered Regressions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(4), pages 817-842, December.
    11. Jamshidian, Mortaza & Liu, Wei & Zhang, Ying & Jamishidian, Farid, 2005. "Simreg: a Software Including Some New Developments in Multiple Comparison and Simultaneous Confidence Bands for Linear Regression Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i02).
    12. Liu, W. & Hayter, A.J. & Piegorsch, W.W. & Ah-Kine, P., 2009. "Comparison of hyperbolic and constant width simultaneous confidence bands in multiple linear regression under MVCS criterion," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1432-1439, August.
    13. W. Liu & F. Bretz & A. J. Hayter & H. P. Wynn, 2009. "Assessing Nonsuperiority, Noninferiority, or Equivalence When Comparing Two Regression Models Over a Restricted Covariate Region," Biometrics, The International Biometric Society, vol. 65(4), pages 1279-1287, December.
    14. Majid Mojirsheibani, 2022. "On the maximal deviation of kernel regression estimators with NMAR response variables," Statistical Papers, Springer, vol. 63(5), pages 1677-1705, October.
    15. Kuriki, Satoshi & Takemura, Akimichi & Taylor, Jonathan E., 2022. "The volume-of-tube method for Gaussian random fields with inhomogeneous variance," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    16. Jamshidian, Mortaza & Liu, Wei & Bretz, Frank, 2010. "Simultaneous confidence bands for all contrasts of three or more simple linear regression models over an interval," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1475-1483, June.

    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:compst:v:40:y:2025:i:1:d:10.1007_s00180-024-01481-6. 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.