The Annals of Statistics

Asymptotic Properties of Maximum Likelihood Estimates in the Mixed Poisson Model

Diane Lambert and Luke Tierney

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Abstract

This paper considers the asymptotic behavior of the maximum likelihood estimators (mle's) of the probabilities of a mixed Poisson distribution with a nonparametric mixing distribution. The vector of estimated probabilities is shown to converge in probability to the vector of mixed probabilities at rate $n^{1/2-\varepsilon}$ for any $\varepsilon > 0$ under a generalized $\chi^2$ distance function. It is then shown that any finite set of the mle's has the same joint limiting distribution as does the corresponding set of sample proportions when the support of the mixing distribution $G_0$ is an infinite set with a known upper bound and $G_0$ satisfies a certain condition at zero.

Article information

Source
Ann. Statist., Volume 12, Number 4 (1984), 1388-1399.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176346799

Digital Object Identifier
doi:10.1214/aos/1176346799

Mathematical Reviews number (MathSciNet)
MR765931

Zentralblatt MATH identifier
0562.62038

JSTOR
links.jstor.org

Subjects
Primary: 62E20: Asymptotic distribution theory
Secondary: 62G05: Estimation

Keywords
Asymptotic normality consistency nonparametric maximum likelihood estimation

Citation

Lambert, Diane; Tierney, Luke. Asymptotic Properties of Maximum Likelihood Estimates in the Mixed Poisson Model. Ann. Statist. 12 (1984), no. 4, 1388--1399. doi:10.1214/aos/1176346799. https://projecteuclid.org/euclid.aos/1176346799


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