Kernel density estimation in adaptive tracking

被引：0

作者：

Bercu, Bernard ^{[1
]}

Portier, Bruno ^{[2
]}

机构：

[1] Univ Bordeaux 1, Inst Math Bordeaux, UMR 5251, 351 Cours Liberat, F-33405 Talence, France

[2] INSA, Dept Gen Math, F-76131 Mont St Aignan, France

来源：

47TH IEEE CONFERENCE ON DECISION AND CONTROL, 2008 (CDC 2008) | 2008年

关键词：

D O I：

10.1109/CDC.2008.4738648

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 associated with the driven noise of multivariate ARMAX models in adaptive tracking. We establish 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：3441 / 3445

页数：5

共 24 条

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