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

Bernard Bercu (1, 2) , Bruno Portier (3)

1 LSP - Laboratoire de Statistique et Probabilités
2 CQFD - Quality control and dynamic reliability
3 LM-Orsay - Laboratoire de Mathématiques d'Orsay

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.

Mots clés: Adaptive control; Kernel density estimation; Goodness-of-fit test

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

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