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A Double Sampling Scheme for Estimating from Misclassified Binomial Data

机译:从误分类二项式数据估计的双重抽样方案

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Two measuring devices are available to classify units into one or two mutually exclusive categories. The first device is an expensive procedure which classifies units correctly; the second device is a cheaper procedure which tends to misclassify units. In order to estimate p, the proportion of units in the population which belong to one of the categories, a double sampling scheme is presented. At the first stage, a sample of N units is taken and the fallible classifications are obtained; at the second stage of subsample of n units is drawn from the main sample and the true classifications are obtained. The maximum likelihood estimate of p is derived along with its asymptotic variance. Optimum values of n and N which minimize the measurement costs for a fixed variance of estimation and which minimize the variance for fixed cost are derived.

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