IDEAS home Printed from https://ideas.repec.org/a/oup/biomet/v96y2009i2p383-398.html
   My bibliography  Save this article

Nonparametric additive regression for repeatedly measured data

Author

Listed:
  • Raymond J. Carroll
  • Arnab Maity
  • Enno Mammen
  • Kyusang Yu

Abstract

We develop an easily computed smooth backfitting algorithm for additive model fitting in repeated measures problems. Our methodology easily copes with various settings, such as when some covariates are the same over repeated response measurements. We allow for a working covariance matrix for the regression errors, showing that our method is most efficient when the correct covariance matrix is used. The component functions achieve the known asymptotic variance lower bound for the scalar argument case. Smooth backfitting also leads directly to design-independent biases in the local linear case. Simulations show our estimator has smaller variance than the usual kernel estimator. This is also illustrated by an example from nutritional epidemiology. Copyright 2009, Oxford University Press.

Suggested Citation

  • Raymond J. Carroll & Arnab Maity & Enno Mammen & Kyusang Yu, 2009. "Nonparametric additive regression for repeatedly measured data," Biometrika, Biometrika Trust, vol. 96(2), pages 383-398.
    • Handle: RePEc:oup:biomet:v:96:y:2009:i:2:p:383-398
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/biomet/asp015
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Zhang, Xiaoke & Zhong, Qixian & Wang, Jane-Ling, 2020. "A new approach to varying-coefficient additive models with longitudinal covariates," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    2. Enno Mammen & Byeong U. Park & Melanie Schienle, 2012. "Additive Models: Extensions and Related Models," SFB 649 Discussion Papers SFB649DP2012-045, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    3. Al Kadiri, M. & Carroll, R.J. & Wand, M.P., 2010. "Marginal longitudinal semiparametric regression via penalized splines," Statistics & Probability Letters, Elsevier, vol. 80(15-16), pages 1242-1252, August.
    4. Shujie Ma & Jeffrey S. Racine, 2012. "Additive Regression Splines With Irrelevant Categorical and Continuous Regressors," Department of Economics Working Papers 2012-07, McMaster University.
    5. Qian Huang & Jinhong You & Liwen Zhang, 2022. "Efficient inference of longitudinal/functional data models with time‐varying additive structure," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(2), pages 744-771, June.
    6. Ma, Haiqiang & Zhu, Zhongyi, 2016. "Continuously dynamic additive models for functional data," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 1-13.

    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:oup:biomet:v:96:y:2009:i:2:p:383-398. 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: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/biomet .

    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.