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June, 1982 On the Asymptotic Normality of Statistics with Estimated Parameters
Ronald H. Randles
Ann. Statist. 10(2): 462-474 (June, 1982). DOI: 10.1214/aos/1176345787


Often a statistic of interest would take the form of a member of a common family, except that some vital parameter is unknown and must be estimated. This paper describes methods for showing the asymptotic normality of such statistics with estimated parameters. Whether or not the limiting distribution is affected by the estimator is primarily a question of whether or not the limiting mean (derived by replacing the estimator by a mathematical variable) has a nonzero derivative with respect to that variable. Section 2 contains conditions yielding the asymptotic normality of $U$-statistics with estimated parameters. These results generalize previous theorems by Sukhatme (1958). As an example, we show the limiting normality of a resubstitution estimator of a correct classification probability when using Fisher's linear discriminant function. The results for $U$-statistics are extended to cover a broad class of families of statistics through the differential. Specifically, conditions are given which yield the asymptotic normality of adaptive $L$-statistics and an example due to de Wet and van Wyk (1979) is examined.


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Ronald H. Randles. "On the Asymptotic Normality of Statistics with Estimated Parameters." Ann. Statist. 10 (2) 462 - 474, June, 1982.


Published: June, 1982
First available in Project Euclid: 12 April 2007

MathSciNet: MR653521
zbMATH: 0493.62022
Digital Object Identifier: 10.1214/aos/1176345787

Primary: 62E20

Keywords: $L$-statistics , $U$-statistics , asymptotic normality , differentiable statistical functions , estimated parameters

Rights: Copyright © 1982 Institute of Mathematical Statistics

Vol.10 • No. 2 • June, 1982
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