The Annals of Statistics

Fourier Methods for Estimating Mixing Densities and Distributions

Cun-Hui Zhang

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Abstract

Let $X_1, X_2, \cdots$ be iid observations from a mixture density $f(x) = \int f(x \mid \theta)dG(\theta)$, where $f(x \mid \theta)$ is a known parametric family of density functions and $G$ is an unknown distribution function. This paper concerns estimating the mixing density $g = G'$ and the mixing distribution $G$. Fourier methods are used to derive kernel estimators, upper bounds for their rates of convergence and lower bounds for the optimal rate of convergence. Sufficient conditions are given under which the kernel estimators are asymptotically normal. Our estimators achieve the optimal rate of convergence $(\log n)^{-1/2}$ for the normal family and $(\log n)^{-1}$ for the Cauchy family.

Article information

Source
Ann. Statist. Volume 18, Number 2 (1990), 806-831.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
http://projecteuclid.org/euclid.aos/1176347627

Digital Object Identifier
doi:10.1214/aos/1176347627

Mathematical Reviews number (MathSciNet)
MR1056338

Zentralblatt MATH identifier
0778.62037

JSTOR
links.jstor.org

Subjects
Primary: 62G05: Estimation
Secondary: 62G20: Asymptotic properties 62E20: Asymptotic distribution theory

Keywords
Mixing distribution kernel estimation contiguity Fourier transformation

Citation

Zhang, Cun-Hui. Fourier Methods for Estimating Mixing Densities and Distributions. Ann. Statist. 18 (1990), no. 2, 806--831. doi:10.1214/aos/1176347627. http://projecteuclid.org/euclid.aos/1176347627.


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