Electronic Journal of Statistics

Estimating the density of a conditional expectation

Samuel G. Steckley, Shane G. Henderson, David Ruppert, Ran Yang, Daniel W. Apley, and Jeremy Staum

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Abstract

In this paper, we analyze methods for estimating the density of a conditional expectation. We compare an estimator based on a straightforward application of kernel density estimation to a bias-corrected estimator that we propose. We prove convergence results for these estimators and show that the bias-corrected estimator has a superior rate of convergence. In a simulated test case, we show that the bias-corrected estimator performs better in a practical example with a realistic sample size.

Article information

Source
Electron. J. Statist., Volume 10, Number 1 (2016), 736-760.

Dates
Received: September 2015
First available in Project Euclid: 22 March 2016

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1458655725

Digital Object Identifier
doi:10.1214/16-EJS1121

Mathematical Reviews number (MathSciNet)
MR3477740

Zentralblatt MATH identifier
06561112

Keywords
Density deconvolution kernel density estimation bias-correction nested simulation repeated measurements

Citation

Steckley, Samuel G.; Henderson, Shane G.; Ruppert, David; Yang, Ran; Apley, Daniel W.; Staum, Jeremy. Estimating the density of a conditional expectation. Electron. J. Statist. 10 (2016), no. 1, 736--760. doi:10.1214/16-EJS1121. https://projecteuclid.org/euclid.ejs/1458655725


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