A DOUBLE SAMPLING SCHEME FOR ESTIMATING FROM MISCLASSIFIED BINOMIAL DATA.
Abstract
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 16, 1969
- Accession Number
- AD0690886
Entities
People
- Aaron Tenenbein
Organizations
- Harvard University