A Biweight Approach to the One-Sample Problem
提出一种类似t统计量的双权统计量，用于在对称厚尾分布下构建置信区间，通过蒙特卡洛模拟评估其在中等样本量下的效率，并针对小样本进行尺度调整。
Abstract A "t"-like statistic, replacing the classical mean by a biweight location estimator in the numerator and the sample variance by a corresponding variance term in the denominator, is proposed as a modification to that used by Gross (1976) and is evaluated for its efficiency in constructing confidence intervals in symmetric, stretched-tailed situations. The one-sample biweight "t" is shown, via Monte Carlo simulations, to be efficient for samples of moderate sizes (in terms of expected length of the confidence intervals). For smaller samples (size five), the sum of the biweight weights is useful in rescaling biweight "t". For several samples of common population width, a root mean square of the variances affords greater stability when the underlying distribution is not extremely stretched-tailed. Key Words: RobustnessLocation and scale estimatesConfidence intervalsStudent's t Monte Carlo