Long memory and self-similar signals: A new recursive maximum likelihood estimator

被引：0

作者：

Noret, E ^{[1
]}

Guglielmi, M ^{[1
]}

机构：

[1] Inst Rech Cybernet Nantes, UMR CNRS 6597, F-44321 Nantes 3, France

来源：

SYSTEM STRUCTURE AND CONTROL 1998 (SSC'98), VOLS 1 AND 2 | 1998年

关键词：

non-stationary models; non-uniform sampling; recursive maximum likelihood estimation; self-similar signals;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper is devoted to the estimation problem of linear non-stationary systems. The basic point of view is the maximum likelihood technic. We design the global (ML) and recursive (RML) algorithms for a class of non-stationary AutoRegressive models. We apply those algorithms to a class of self-similar and long memory signals. To prevent divergence of the (RML) algorithm it is necessary to compute the analytic gradient and hessien of the criteria. Finally we present some results issued from simulations with comparisons between (ML) and (RML) methods. Copyright (C) 1998 IFAC.

引用

页码：275 / 280

页数：6

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