Open Access
2014 Self-Consistent Density Estimation in the Presence of Errors-in-Variables
Junhua Zhang, Yuping Hu, Sanying Feng
Abstr. Appl. Anal. 2014: 1-12 (2014). DOI: 10.1155/2014/958702


This paper considers the estimation of the common probability density of independent and identically distributed variables observed with additive measurement errors. The self-consistent estimator of the density function is constructed when the error distribution is known, and a modification of the self-consistent estimation is proposed when the error distribution is unknown. The consistency properties of the proposed estimators and the upper bounds of the mean square error and mean integrated square error are investigated under some suitable conditions. Simulation studies are carried out to assess the performance of our proposed method and compare with the usual deconvolution kernel method. Two real datasets are analyzed for further illustration.


Download Citation

Junhua Zhang. Yuping Hu. Sanying Feng. "Self-Consistent Density Estimation in the Presence of Errors-in-Variables." Abstr. Appl. Anal. 2014 1 - 12, 2014.


Published: 2014
First available in Project Euclid: 27 February 2015

zbMATH: 07023401
MathSciNet: MR3292985
Digital Object Identifier: 10.1155/2014/958702

Rights: Copyright © 2014 Hindawi

Vol.2014 • 2014
Back to Top