Kernel estimates of functions and their derivatives with applications

Alexander A. Georgiev

Statistics & Probability Letters, 1984, vol. 2, issue 1, 45-50

Abstract: The problem of the estimation of functions and their derivatives from noisy observations is considered. The new kernel estimate is shown to be consistent in the mean square sense and an exact bound on the uniform mean squared error is given. An application to the system identification is discussed.

Keywords: regression; function; derivatives; of; regression; function; kernel; estimation; curve; fitting; system; identification; Lipschitz; function; mean; square; error; convergence (search for similar items in EconPapers)
Date: 1984
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