The Annals of Statistics

Maximum Likelihood Estimation with Partially Censored Data

Aad van der Vaart

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Abstract

Suppose one observes independent samples of size $n$ from both the mixture density $\int p(x \mid z) d\eta(z)$ and from the distribution $\eta$. The kernel $p(x \mid z)$ is known. We show asymptotic normality and efficiency of the maximum likelihood estimator for $\eta$.

Article information

Source
Ann. Statist. Volume 22, Number 4 (1994), 1896-1916.

Dates
First available in Project Euclid: 11 April 2007

Permanent link to this document
http://projecteuclid.org/euclid.aos/1176325763

Digital Object Identifier
doi:10.1214/aos/1176325763

Mathematical Reviews number (MathSciNet)
MR1329174

Zentralblatt MATH identifier
0831.62027

JSTOR
links.jstor.org

Subjects
Primary: 62G20: Asymptotic properties
Secondary: 62F12: Asymptotic properties of estimators

Keywords
Mixture model maximum likelihood efficiency deconvolution censoring

Citation

van der Vaart, Aad. Maximum Likelihood Estimation with Partially Censored Data. Ann. Statist. 22 (1994), no. 4, 1896--1916. doi:10.1214/aos/1176325763. http://projecteuclid.org/euclid.aos/1176325763.


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