Sequential Estimation of the Mean of a Multinormal Population
将Robbins和Starr提出的正态总体均值序贯估计方法推广到多元情形，推导了停止时间的精确分布，并给出可编程的递归计算方法，证明了风险效率与总体维度的关系。
Abstract This article is a multivariate generalization of the sequential sampling procedure developed by Robbins (1959) and extended by Starr (1966b) for estimating the mean of a normal population when the scale parameter is unknown. The exact distribution of the stopping time is derived for the sequential procedure, and a recursive method for computing the distribution of the stopping time is proposed that can be easily programmed for mechanical calculation. It is shown that the “risk efficiency” of the sequential procedure is a function of the dimension of the population.