Open Access
Translator Disclaimer
December, 1984 Asymptotic Efficiency of Estimators of Functionals of Mixed Distributions
Luke Tierney, Diane Lambert
Ann. Statist. 12(4): 1380-1387 (December, 1984). DOI: 10.1214/aos/1176346798


Suppose $F$ is a mixture of a known parametric family of distributions with an unknown nonparametric mixing distribution. Consider the problem of estimating the value $T(dF)$ of a smooth functional $T$ at the mixed distribution $F$. One nonparametric estimator of $T(dF)$ is $T(dF_n)$ where $F_n$ denotes the empirical distribution. Under a differentiability condition on $T$ given in this note, $T(dF_n)$ is shown to be fully efficient asymptotically in the sense that the limiting distribution of any other estimator of $T(dF)$ that is regular in the sense of Hajek and Beran can be expressed as a convolution of the limiting normal distribution of $T(dF_n)$ with another distribution. The differentiability condition is verified for the case that the parametric family being mixed is a one-parameter exponential family and the support of the mixing distribution is an infinite set with a finite accumulation point. Regularity is verified for the maximum likelihood estimator of the probability of zero in a mixed Poisson distribution under certain conditions on the mixing distribution. Moreover, the maximum likelihood estimator and the sample proportion of zeroes are both shown to be fully efficient in this example.


Download Citation

Luke Tierney. Diane Lambert. "Asymptotic Efficiency of Estimators of Functionals of Mixed Distributions." Ann. Statist. 12 (4) 1380 - 1387, December, 1984.


Published: December, 1984
First available in Project Euclid: 12 April 2007

zbMATH: 0557.62046
MathSciNet: MR760695
Digital Object Identifier: 10.1214/aos/1176346798

Primary: 62G20
Secondary: 62G05

Keywords: asymptotic variance bound , regular estimator , representation theorem

Rights: Copyright © 1984 Institute of Mathematical Statistics


Vol.12 • No. 4 • December, 1984
Back to Top