Abstract and Applied Analysis

Self-Consistent Density Estimation in the Presence of Errors-in-Variables

Junhua Zhang, Yuping Hu, and Sanying Feng

Full-text: Access denied (no subscription detected)

We're sorry, but we are unable to provide you with the full text of this article because we are not able to identify you as a subscriber. If you have a personal subscription to this journal, then please login. If you are already logged in, then you may need to update your profile to register your subscription. Read more about accessing full-text


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.

Article information

Abstr. Appl. Anal., Volume 2014 (2014), Article ID 958702, 12 pages.

First available in Project Euclid: 27 February 2015

Permanent link to this document

Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier


Zhang, Junhua; Hu, Yuping; Feng, Sanying. Self-Consistent Density Estimation in the Presence of Errors-in-Variables. Abstr. Appl. Anal. 2014 (2014), Article ID 958702, 12 pages. doi:10.1155/2014/958702. https://projecteuclid.org/euclid.aaa/1425049894

Export citation