Recursive estimation of nonparametric regression with functional covariate

Authors:
Aboubacar Amiri;Christophe Crambes;Baba Thiam
Affiliations:
-;-;-
Venue:
Computational Statistics & Data Analysis
Year:
2014

Citing 3
Cited 1

Recursive probability density estimation for weakly dependent stationary processes

IEEE Transactions on Information Theory
Simultaneous non-parametric regressions of unbalanced longitudinal data

Computational Statistics & Data Analysis
Editorial: Statistics for Functional Data

Computational Statistics & Data Analysis

Theoretical and practical aspects of the quadratic error in the local linear estimation of the conditional density for functional data

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.03

Visualization

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.