Nonparametric recursive estimation of the derivative of the regression function with application to sea shores water quality

Bernard Bercu (), Sami Capderou () and Gilles Durrieu ()
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Bernard Bercu: Université de Bordeaux
Sami Capderou: Université de Bordeaux
Gilles Durrieu: Université de Bretagne Sud

Statistical Inference for Stochastic Processes, 2019, vol. 22, issue 1, No 2, 17-40

Abstract: Abstract This paper is devoted to the nonparametric estimation of the derivative of the regression function in a nonparametric regression model. We implement a very efficient and easy to handle statistical procedure based on the derivative of the recursive Nadaraya–Watson estimator. We establish the almost sure convergence as well as the asymptotic normality for our estimates. We also illustrate our nonparametric estimation procedure on simulated data and real life data associated with sea shores water quality and valvometry.

Keywords: Application and case studies; Mathematical statistics; Nonparametric methods; Smoothing and nonparametric regression (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11203-017-9169-1

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