Translator Disclaimer
2018 Conditional kernel density estimation for some incomplete data models
Ting Yan, Liangqiang Qu, Zhaohai Li, Ao Yuan
Electron. J. Statist. 12(1): 1299-1329 (2018). DOI: 10.1214/18-EJS1423

Abstract

A class of density estimators based on observed incomplete data are proposed. The method is to use a conditional kernel, defined as the expectation of a given kernel for the complete data conditioning on the observed data, to construct the density estimator. We study such kernel density estimators for several commonly used incomplete data models and establish their basic asymptotic properties. Some characteristics different from the classical kernel estimators are discovered. For instance, the asymptotic results of the proposed estimator do not depend on the choice of the kernel $k(\cdot )$. Simulation study is conducted to evaluate the performance of the estimator and compared with some exising methods.

Citation

Download Citation

Ting Yan. Liangqiang Qu. Zhaohai Li. Ao Yuan. "Conditional kernel density estimation for some incomplete data models." Electron. J. Statist. 12 (1) 1299 - 1329, 2018. https://doi.org/10.1214/18-EJS1423

Information

Received: 1 November 2016; Published: 2018
First available in Project Euclid: 28 April 2018

zbMATH: 06875401
MathSciNet: MR3795749
Digital Object Identifier: 10.1214/18-EJS1423

JOURNAL ARTICLE
31 PAGES


SHARE
Vol.12 • No. 1 • 2018
Back to Top