Abstract and Applied Analysis

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

Junhua Zhang, Yuping Hu, and Sanying Feng

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Abstract

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

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

Dates
First available in Project Euclid: 27 February 2015

Permanent link to this document
https://projecteuclid.org/euclid.aaa/1425049894

Digital Object Identifier
doi:10.1155/2014/958702

Mathematical Reviews number (MathSciNet)
MR3292985

Zentralblatt MATH identifier
07023401

Citation

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


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