Estimating the density of a conditional expectation

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

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)736-760
Number of pages25
JournalElectronic Journal of Statistics
Volume10
Issue number1
DOIs
StatePublished - 2016

Keywords

  • Bias-correction
  • Density deconvolution
  • Kernel density estimation
  • Nested simulation
  • Repeated measurements

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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