观测数据野值剔除的TBS-LS逼近方法

Elimination of Outliers in Observation Data with TBS-LS Approximation

提出了以最小二乘逼近方法为基础的数据野值判别与剔除算法。利用TBS（三角B样条）曲线同时具有局部性和整体性的优越性构造最小二乘拟合算法,并结合偏度分析与残量分析误差方法,在给定范数意义下的评价系统中,可以得到TBS-LS（最小二乘三角B样条）拟合曲线,从而可以更好地识别并剔除野值。最后给出算法以及主要结果,通过实例说明方法的有效性。

This paper presents an outlier identification and elimination algorithm with least-square triangle B-Spline based on the theory of approximation.Triangle B-Spline with both whole and local properties of superiority is utilized and constructional algorithm analysis of error residual and skewness is given.Triangle B-Spline curve fitting with the given norm in the evaluation system enables better identification and elimination of outliers in observation data.Finally,the effectiveness of the method is illustrated with some examples.