Functional semiparametric partially linear model with autoregressive errors.
J MULTIVARIATE ANAL
307 - 315.
In this paper, we introduce a functional semiparametric model, where a real-valued random variable is explained by the sum of a unknown linear combination of the components of a multivariate random variable and an unknown transformation of a functional random variable. The errors can be autocorrelated. We focus here on the parametric estimation of the coefficients in the linear combination. First, we use a nonparametric kernel method to remove the effect of the functional explanatory variable. Then, we use generalized least squares approach to obtain an estimator of these coefficients. Under some technical assumptions, we prove consistency and asymptotic normality of our estimator. Finally,we present Monte Carlo simulations that illustrate these characteristics. (C) 2008 Elsevier Inc. All rights reserved.
|Title:||Functional semiparametric partially linear model with autoregressive errors|
|Keywords:||Functional data, Semiparametric regression, Autocorrelation, REGRESSION|
|UCL classification:||UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Maths and Physical Sciences
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