System error variance tuning in state-space models

被引：0
|
作者：
Mantovan, P [1 ]
Pastore, A [1 ]
机构：
[1] Univ Ca Foscari Venezia, Dipartimento Stat, I-30125 Venice, Italy
来源：
BETWEEN DATA SCIENCE AND APPLIED DATA ANALYSIS | 2003年
关键词：
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
The multivariate dynamic regression model is a particular specification of the dynamic linear model. For this model, we propose a recursive equation for the estimation of the system error variance matrix. The solution can be used when more observation axe available at each state of the system. In these cases, the algorithm allows to define a recursive procedure for the estimate of both the state vector (the regression coefficients) and the other hyperparameters of the model. The performances of the proposed method are evaluated by means of Monte Carlo experiments.
引用
收藏
页码：313 / 320
页数：8
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