Recursive estimation of nonparametric regression with functional covariate

Aboubacar Amiri, Christophe Crambes and Baba Thiam ()

Computational Statistics & Data Analysis, 2014, vol. 69, issue C, 154-172

Abstract: The main purpose is to estimate the regression function of a real random variable with functional explanatory variable by using a recursive nonparametric kernel approach. The mean square error and the almost sure convergence of a family of recursive kernel estimates of the regression function are derived. These results are established with rates and precise evaluation of the constant terms. Also, a central limit theorem for this class of estimators is established. The method is evaluated on simulations and real dataset studies.

Keywords: Functional data; Recursive kernel estimators; Regression function; Quadratic error; Almost sure convergence; Asymptotic normality (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:69:y:2014:i:c:p:154-172

DOI: 10.1016/j.csda.2013.07.030

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