Kernel density estimation and goodness-of-fit test in adaptive tracking

Abstract : We investigate the asymptotic properties of a recursive kernel density estimator associated with the driven noise of a linear regression in adaptive tracking. We provide an almost sure pointwise and uniform strong law of large numbers as well as a pointwise and multivariate central limit theorem.
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Contributor : Bernard Bercu <>
Submitted on : Wednesday, October 26, 2005 - 8:58:26 PM
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  • HAL Id : hal-00012682, version 1

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Bernard Bercu, Bruno Portier. Kernel density estimation and goodness-of-fit test in adaptive tracking. SIAM Journal on Control and Optimization, Society for Industrial and Applied Mathematics, 2008, 47, pp.2440-2457. ⟨hal-00012682⟩

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