IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v67y1998i1p49-71.html
   My bibliography  Save this article

Functional ANOVA Models for Generalized Regression

Author

Listed:
  • Huang, Jianhua Z.

Abstract

The Functional ANOVA model is considered in the context of generalized regression, which includes logistic regression, probit regression, and Poisson regression as special cases. The multivariate predictor function is modeled as a specified sum of a constant term, main effects, and selected interaction terms. Maximum likelihood estimate is used, where the maximization is taken over a suitably chosen approximating space. The approximating space is constructed from virtually arbitrary linear spaces of functions and their tensor products and is compatible with the assumed ANOVA structure on the predictor function. Under mild conditions, the maximum likelihood estimate is consistent and the components of the estimate in an appropriately defined ANOVA decomposition are consistent in estimating the corresponding components of the predictor function. When the predictor function does not satisfy the assumed ANOVA form, the estimate converges to its best approximation of that form relative to the expected log-likelihood. A rate of convergence result is obtained, which reinforces the intuition that low-order ANOVA modeling can achieve dimension reduction and thus overcome the curse of dimensionality.

Suggested Citation

  • Huang, Jianhua Z., 1998. "Functional ANOVA Models for Generalized Regression," Journal of Multivariate Analysis, Elsevier, vol. 67(1), pages 49-71, October.
    • Handle: RePEc:eee:jmvana:v:67:y:1998:i:1:p:49-71
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047-259X(98)91753-5
    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.

    Citations

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


    Cited by:

    1. Yu, Lili & Peace, Karl E., 2012. "Spline nonparametric quasi-likelihood regression within the frame of the accelerated failure time model," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2675-2687.
    2. Lan Xue & Hua Liang, 2010. "Polynomial Spline Estimation for a Generalized Additive Coefficient Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(1), pages 26-46, March.
    3. Huang, Jianhua Z., 2003. "Asymptotics for polynomial spline regression under weak conditions," Statistics & Probability Letters, Elsevier, vol. 65(3), pages 207-216, November.
    4. Cui, Xia & Zhao, Weihua & Lian, Heng & Liang, Hua, 2019. "Pursuit of dynamic structure in quantile additive models with longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 130(C), pages 42-60.
    5. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    6. Chen, Xiaohong, 2007. "Large Sample Sieve Estimation of Semi-Nonparametric Models," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 76, Elsevier.
    7. Alexander Caicedo & Carolina Varon & Sabine Van Huffel & Johan A K Suykens, 2019. "Functional form estimation using oblique projection matrices for LS-SVM regression models," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-21, June.
    8. Miao Yang & Lan Xue & Lijian Yang, 2016. "Variable selection for additive model via cumulative ratios of empirical strengths total," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(3), pages 595-616, September.

    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:jmvana:v:67:y:1998:i:1:p:49-71. 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: 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.