On the Small-Sample Properties of the Olkin-Sobel-Tong Estimator of the Probability of Correct Selection
研究了Olkin-Sobel-Tong估计量在小样本下的表现，通过解析和蒙特卡洛模拟发现其在k≥2个正态总体均值比较中存在严重缺陷，对统计学者评估该估计量适用性有参考价值。
Abstract In the problem of selecting the best of k populations, Olkin, Sobel, and Tong (1976) have introduced the important idea of a posteriori analysis of the data (as opposed to the usual formulation), in which design of the experiment is the major consideration. They considered the large-sample properties of an estimator that has been discussed further by Gibbons, Olkin, and Sobel (1977), Gupta and Panchapakesan (1979), and Tong (1980). In this article we study the small-sample performance of the Olkin, Sobel, and Tong estimator, analytically for k = 2 populations and via Monte Carlo simulation for k ≥ 2 populations in the normal means, common, known variance case. This small-sample performance is found to have some serious shortcomings.