Extrapolation of Linear Estimates to Larger Sample Sizes
研究了McCool方法，即利用小样本线性估计的系数来构造大样本的线性估计，并基于U统计量理论分析了该方法的效率，发现对逻辑分布和帕累托分布能产生渐近有效的参数估计。
Abstract A study is performed of McCool's method for constructing linear estimates from order statistics when the coefficients of a linear estimate for a smaller sample size are available. The efficiency of the method is investigated, using the theory of U statistics. Among other things, it is shown that in the case of the logistic distribution, the method produces an asymptotically efficient location parameter estimate and, in the case of the Pareto distribution, an asymptotically efficient scale parameter estimate.