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A Normal Limit Law for a Nonparametric Estimator of the Coverage of a Random Sample
Warren W. Esty
The Annals of Statistics
Vol. 11, No. 3 (Sep., 1983), pp. 905-912
Published by: Institute of Mathematical Statistics
Stable URL: http://www.jstor.org/stable/2240652
Page Count: 8
You can always find the topics here!Topics: Confidence interval, Statism, Statistical estimation, Eigenfunctions, Perceptron convergence procedure, Estimators, Statistical variance, Population estimates, Population parameters
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The coverage of a multinomial random sample is the sum of the probabilities of the observed classes. A normal limit law is rigorously proved for Good's (1953) coverage estimator. The result is valid under very general conditions and all terms except the coverage itself are observable. Nevertheless the implied confidence intervals are not much wider than those developed under restrictive assumptions such as in the classical occupancy problem. The asymptotic variance is somewhat unexpected. The proof utilizes a method of Holst (1979).
The Annals of Statistics © 1983 Institute of Mathematical Statistics