The Annals of Statistics

On the Asymptotic Normality of Statistics with Estimated Parameters

Ronald H. Randles

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Abstract

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.

Article information

Source
Ann. Statist., Volume 10, Number 2 (1982), 462-474.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176345787

Digital Object Identifier
doi:10.1214/aos/1176345787

Mathematical Reviews number (MathSciNet)
MR653521

Zentralblatt MATH identifier
0493.62022

JSTOR
links.jstor.org

Subjects
Primary: 62E20: Asymptotic distribution theory

Keywords
Asymptotic normality estimated parameters $U$-statistics $L$-statistics differentiable statistical functions

Citation

Randles, Ronald H. On the Asymptotic Normality of Statistics with Estimated Parameters. Ann. Statist. 10 (1982), no. 2, 462--474. doi:10.1214/aos/1176345787. https://projecteuclid.org/euclid.aos/1176345787


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