一种新的数据二阶特征估计算法及其在数据校正中的应用

A New Estimation Algorithm for Data's Second Order Character and Its Application in Data Reconciliation

在许多数据分析,尤其是数据校正过程中,需要知道数据的二阶特征。现行的二阶特征算法通常是对方差的估计,算法存在共同的局限性,即假定样本中不含有显著误差,并且服从正态分布。这种假定在许多实际情况中是不能满足的,从而使算法的应用受到了限制。针对常用算法的局限性,本文提出了一种新的数据二阶特征估计算法。该算法基于序列的关联性理论,通过对信号或然误差的实时估计实现对数据等效二阶特征的估计。理论分析和仿真实例验证了新算法的有效性。

In many process of data analysis,especially in data reconciliation,it is required to know the second order character of data.It is usual to estimate the error variance in the existing method of second order character.These algorithms have common localization limitation,i.e.,assume that there don′t exist gross error in samples and samples′ distribution is normal,which connot be satisfied in many real applications.Aiming at the above shortcomings,this paper proposed a new estimation algorithm for data′s second order character.In the proposed the probable variance is utilized to estimate data′s second order character by basing on the relationship theory of series.Both theory analysis and simulation results validate effectiveness of the new algorithm.