A comparison between parallel algorithms for system parameter estimation in dynamic linear models

被引：0

作者：

Mantovan, P ^{[1
]}

Pastore, A ^{[1
]}

Tonellato, S ^{[1
]}

机构：

[1] Univ Venice, Dipartimento Stat, I-30125 Venice, Italy

来源：

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY | 1999年 / 15卷 / 04期

关键词：

dynamic linear models; system parameter estimation; Kalman filter; parallel algorithms;

D O I：

10.1002/(SICI)1526-4025(199910/12)15:4<369::AID-ASMB400>3.0.CO;2-Q

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

When dealing with high-frequency time series, statistical procedures giving reliable estimates of unknown parameters and forecasts in real time are required. This is why recursive estimation methods are usually preferred to maximum-likelihood estimators. In the paper, a recursive estimation algorithm for the system parameter of dynamic linear models is proposed. A comparison with some other algorithms is given via Monte Carlo simulations. Consistency properties of the algorithms are also empirically verified. Copyright (C) 1999 John Wiley & Sons, Ltd.

引用

页码：369 / 378

页数：10

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