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Double-smoothing for varying coefficient models

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  • Wan Tang
  • Guoxin Zuo
  • Hua He

Abstract

Moderation analyses are widely used in biomedical and psychosocial research to investigate differential treatment effects, with moderators frequently identified through testing the significance of the interaction between the predictor and the potential moderator under strong parametric assumptions. Without imposing any parametric forms on how the moderators may affect the relationship between predictors and responses, varying coefficient models address this fundamental problem of strong parametric assumptions with the current practice of moderation analysis and provide a much broader class of models for complex moderation relationships. Local polynomial, especially local linear (LL), methods are commonly used in estimating the varying coefficient models. Recently, a double-smoothing (DS) LL method has been proposed for nonparametric regression models, with nice properties compared to LL and local cubic (LC) methods. In this paper, we generalise DS to varying coefficient models, and show that it holds similar advantages over LL and LC methods.

Suggested Citation

  • Wan Tang & Guoxin Zuo & Hua He, 2011. "Double-smoothing for varying coefficient models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(4), pages 917-926.
  • Handle: RePEc:taf:gnstxx:v:23:y:2011:i:4:p:917-926
    DOI: 10.1080/10485252.2011.588707
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    References listed on IDEAS

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    1. Zhang, Wenyang & Lee, Sik-Yum & Song, Xinyuan, 2002. "Local Polynomial Fitting in Semivarying Coefficient Model," Journal of Multivariate Analysis, Elsevier, vol. 82(1), pages 166-188, July.
    2. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167.
    3. Zhang, Wenyang & Lee, Sik-Yum, 2000. "Variable Bandwidth Selection in Varying-Coefficient Models," Journal of Multivariate Analysis, Elsevier, vol. 74(1), pages 116-134, July.
    4. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506.
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    1. Holtzblatt, Mark & Tschakert, Norbert, 2011. "Expanding your accounting classroom with digital video technology," Journal of Accounting Education, Elsevier, vol. 29(2), pages 100-121.

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