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Local polynomial estimation of the mean function and itsderivatives based on functional data and regular designs

Published online by Cambridge University Press:  29 October 2014

Karim Benhenni  and
David Degras
Karim Benhenni
Affiliation:
Laboratoire LJK UMR CNRS 5224, Université de Grenoble, 38040 Grenoble, France. [email protected]
David Degras
Affiliation:
Department of Mathematical Sciences, DePaul University, Chicago 60614, Illinois, USA; [email protected]
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Abstract

We study the estimation of the mean function of a continuous-time stochastic process andits derivatives. The covariance function of the process is assumed to be nonparametric andto satisfy mild smoothness conditions. Assuming that n independent realizationsof the process are observed at a sampling design of size N generated by a positivedensity, we derive the asymptotic bias and variance of the local polynomial estimator asn,Nincrease to infinity. We deduce optimal sampling densities, optimal bandwidths, andpropose a new plug-in bandwidth selection method. We establish the asymptotic performanceof the plug-in bandwidth estimator and we compare, in a simulation study, its performancefor finite sizes n,N to the cross-validation and the optimalbandwidths. A software implementation of the plug-in method is available in the Renvironment.

Keywords

Local polynomial smoothingderivative estimationfunctional datasampling densityplug-in bandwidth
Type
Research Article
Information
ESAIM: Probability and Statistics , Volume 18 , 2014 , pp. 881 - 899
Copyright
© EDP Sciences, SMAI 2014

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