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Moments, errors, asymptotic normality and large deviation principle in nonparametric functional regression

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  • Geenens, Gery

Abstract

Recently, some nonparametric regression ideas have been extended to the functional context, allowing infinite-dimensional regressors. This paper gives a deep asymptotic study of the functional Nadaraya–Watson estimator, including moments of all orders, errors, asymptotic distribution and large deviation rate.

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  • Geenens, Gery, 2015. "Moments, errors, asymptotic normality and large deviation principle in nonparametric functional regression," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 369-377.
    • Handle: RePEc:eee:stapro:v:107:y:2015:i:c:p:369-377
    DOI: 10.1016/j.spl.2015.09.015
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    References listed on IDEAS

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    1. Masry, Elias, 2005. "Nonparametric regression estimation for dependent functional data: asymptotic normality," Stochastic Processes and their Applications, Elsevier, vol. 115(1), pages 155-177, January.
    2. Frédéric Ferraty & Ingrid Van Keilegom & Philippe Vieu, 2010. "On the Validity of the Bootstrap in Non‐Parametric Functional Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(2), pages 286-306, June.
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    8. J. Fan & J.‐T. Zhang, 2000. "Two‐step estimation of functional linear models with applications to longitudinal data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 303-322.
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    Cited by:

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    2. Philip T. Reiss & Jeff Goldsmith & Han Lin Shang & R. Todd Ogden, 2017. "Methods for Scalar-on-Function Regression," International Statistical Review, International Statistical Institute, vol. 85(2), pages 228-249, August.

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