整修平均数是为对称的分布的地点的最普通的评估者之一,其效果取决于整齐的率是否匹配污染数据的比例。基于整修变化功能的曲线的几何特征,作者建议二种有点吝啬的算法。评估者的精确性与另外的使用得经常的估计的相比,例如样品吝啬的、整修平均数, trimean,并且中部,借助于模拟方法。结果证明整修的适应衍生物优化的精确性意味着方法在中等污染的情况下接近最佳表演(污染率是不到 50%) 。在高污染状况下面(污染率等于 80%) ,估计的表演比得上的中部并且比另外的对应物优异。
The trimmed mean is one of the most common estimators of location for symmetrical distributions, whose effect depends on whether the trim rate matches the proportion of contaminated data. Based on the geometric characteristics of the curve of the trimmed variance function, the authors propose two kinds of adaptive trimmed mean algorithms. The accuracy of the estimators is compared with that of other often-used estimates, such as sample mean, trimmed mean, trimean, and median, by means of simulation method. The results show that the accuracy of the adaptive derivative optimization trimmed mean method is close to the optimum performance in case of medium contamination (the contamination rate is less than 50%). Under high contamination situation (the contamination rate equals 8070), the performance of the estimates is comparable to that of the median and is superior to other counterparts.