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Nonparametric estimation of varying-coefficient single-index models

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  • Young-Ju Kim

Abstract

The varying-coefficient single-index model has two distinguishing features: partially linear varying-coefficient functions and a single-index structure. This paper proposes a nonparametric method based on smoothing splines for estimating varying-coefficient functions and an unknown link function. Moreover, the average derivative estimation method is applied to obtain the single-index parameter estimates. For interval inference, Bayesian confidence intervals were obtained based on Bayes models for varying-coefficient functions and the link function. The performance of the proposed method is examined both through simulations and by applying it to Boston housing data.

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  • Young-Ju Kim, 2015. "Nonparametric estimation of varying-coefficient single-index models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(2), pages 281-291, February.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:2:p:281-291
    DOI: 10.1080/02664763.2014.947358
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