IDEAS home Printed from https://ideas.repec.org/a/spr/aistmt/v52y2000i3p519-543.html
   My bibliography  Save this article

A Two-Step Smoothing Method for Varying-Coefficient Models with Repeated Measurements

Author

Listed:
  • Colin Wu
  • Kai Yu
  • Chin-Tsang Chiang

Abstract

No abstract is available for this item.

Suggested Citation

  • Colin Wu & Kai Yu & Chin-Tsang Chiang, 2000. "A Two-Step Smoothing Method for Varying-Coefficient Models with Repeated Measurements," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 52(3), pages 519-543, September.
    • Handle: RePEc:spr:aistmt:v:52:y:2000:i:3:p:519-543
    DOI: 10.1023/A:1004125621021
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1023/A:1004125621021
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1023/A:1004125621021?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.

    Citations

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


    Cited by:

    1. Lee, Kyeongeun & Lee, Young K. & Park, Byeong U. & Yang, Seong J., 2018. "Time-dynamic varying coefficient models for longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 50-65.
    2. Chin-Tsang Chiang, 2005. "Comparisons between simultaneous and componentwise splines for varying coefficient models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 57(4), pages 637-653, December.
    3. Linjun Tang & Zhangong Zhou, 2015. "Weighted local linear CQR for varying-coefficient models with missing covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(3), pages 583-604, September.
    4. Li, XiaoLi & You, JinHong, 2012. "Error covariance matrix correction based approach to functional coefficient regression models with generated covariates," Journal of Multivariate Analysis, Elsevier, vol. 107(C), pages 263-281.
    5. Shu Jiang & Yijun Xie & Graham A. Colditz, 2021. "Functional ensemble survival tree: Dynamic prediction of Alzheimer’s disease progression accommodating multiple time‐varying covariates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 66-79, January.
    6. Yuan Yang & Ziyang Pan & Jian Kang & Chad Brummett & Yi Li, 2023. "Simultaneous selection and inference for varying coefficients with zero regions: a soft‐thresholding approach," Biometrics, The International Biometric Society, vol. 79(4), pages 3388-3401, December.
    7. Shakhawat Hossain & Le An Lac, 2021. "Optimal shrinkage estimations in partially linear single-index models for binary longitudinal data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(4), pages 811-835, December.
    8. Colin O. Wu & Kai F. Yu, 2002. "Nonparametric Varying-Coefficient Models for the Analysis of Longitudinal Data," International Statistical Review, International Statistical Institute, vol. 70(3), pages 373-393, December.
    9. Jason P. Estes & Danh V. Nguyen & Lorien S. Dalrymple & Yi Mu & Damla Şentürk, 2014. "Cardiovascular event risk dynamics over time in older patients on dialysis: A generalized multiple-index varying coefficient model approach," Biometrics, The International Biometric Society, vol. 70(3), pages 751-761, September.
    10. A. Antoniadis & I. Gijbels & S. Lambert-Lacroix, 2014. "Penalized estimation in additive varying coefficient models using grouped regularization," Statistical Papers, Springer, vol. 55(3), pages 727-750, August.
    11. Hans-Georg Müller & Wenjing Yang, 2010. "Dynamic relations for sparsely sampled Gaussian processes," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(1), pages 1-29, May.
    12. Byeong U. Park & Enno Mammen & Young K. Lee & Eun Ryung Lee, 2015. "Varying Coefficient Regression Models: A Review and New Developments," International Statistical Review, International Statistical Institute, vol. 83(1), pages 36-64, April.
    13. Na Li & Xingzhong Xu & Xuhua Liu, 2011. "Testing the constancy in varying-coefficient regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 74(3), pages 409-438, November.

    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:aistmt:v:52:y:2000:i:3:p:519-543. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.