中文说明：应用背景的一维对称分布的位置的参数估计是最容易和最全面地研究统计中的问题。这是一个基准来说明和研究新的想法估计这可能推广到更复杂的情况，以便更好地了解估计问题一般来说关键技术我们进行广泛的模拟研究平滑的M-估计（包括平滑胡贝尔M估计和平滑的中位数）进行比较的最大似然估计和皮特曼估计小样本正常，胡贝尔最小有利，双指数和已知规模的柯西分布。
English Description：
Application backgroundThe parametric estimation of the location of a one-dimensional symmetric distribution is among the easiest and most comprehensively worked on problems in statistics. It is a benchmark to illustrate and investigate new ideas in estimation which may generalize to more complicated situations in order to gain a better understanding about estimation problems in general. Key TechnologyWe carried out an extensive simulation study in which smoothed M-estimators (including the smoothed Huber M-estimator and a smoothed median) were compared to the ML-estimators and Pitman estimators for small samples from the normal, the Huber least favourable, the double exponential and the Cauchy distribution with known scale.