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.
Citation
Samuel G. Steckley. Shane G. Henderson. David Ruppert. Ran Yang. Daniel W. Apley. Jeremy Staum. "Estimating the density of a conditional expectation." Electron. J. Statist. 10 (1) 736 - 760, 2016. https://doi.org/10.1214/16-EJS1121
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