KERNEL DENSITY ESTIMATION AND GOODNESS-OF-FIT TEST IN ADAPTIVE TRACKING

被引：5

作者：

Bercu, Bernard ^{[1
,2
]}

Portier, Bruno ^{[3
]}

机构：

[1] Univ Bordeaux 1, Inst Math Bordeaux, UMR 5251, F-33405 Talence, France

[2] INRIA Bordeaux Sud Ouest, Team CQFD, F-33405 Talence, France

[3] INSA Rouen, Math Lab, Dept Genie Math, LMI,EA 3226, F-76131 Mont St Aignan, France

来源：

SIAM JOURNAL ON CONTROL AND OPTIMIZATION | 2008年 / 47卷 / 05期

关键词：

adaptive control; kernel density estimation; goodness-of-fit test;

D O I：

10.1137/070694739

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We investigate the asymptotic properties of a recursive kernel density estimator of the driven noise of multivariate ARMAX models 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. We also carry out a goodness-of-fit test together with some simulation experiments.

引用

页码：2440 / 2457

页数：18

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