AR模式误差修正方程参数抗差估计

Robust estimation of parameters for AR model error correction equation

把抗差理论引入自回归(Auto Regressive,简称AR)模型的参数估计中,利用抗差系统具有的抗差能力,可以阻止非正常因素进入系统,保证洪水预报精度.为此,首先介绍几种常用估计方案(Huber估计、IGG估计)的抗差特征函数,然后与传统的最小二乘法进行比较,最后给出算例.算例表明:对于正常观测值,采用Huber法、IGG法及LSM法均能取得比较满意的结果;如观测值中存在粗差(非正常值),则用LSM法估计的结果就很不合理,而用Huber法及IGG法能取得较好结果.

The theory of robust estimation is introduced into parameter estimation of the autoregressive model. The robust system can prevent abnormal factors from entering the flood system, so as to ensure the accuracy of flood forecast. Furthermore, some estimation methods commonly used, including the Huber estimation and IGG estimation, are introduced and compared with the Least Square Method (LSM). Examples show that, with normal observed data, the results obtained by the three methods are satisfactory, however, if the observed data are abnormal, the estimated result by LSM is not satisfactory, while the results from the other two methods are still satisfactory.