Nonparametric deconvolution of density estimation based on observed sums

Albert Vexler, Aiyi Liu, Enrique Schisterman

Research output: Contribution to journalArticlepeer-review


This paper develops a methodology for distribution-free estimation of a density function based on observed sums or pooled data. The proposed methods employ a Fourier approach to nonparametric deconvolution of a density estimate. Asymptotic normality is established and an upper bound for the integrated absolute error is given for the proposed density estimator. Monte Carlo simulations are used to examine the performance of the density estimators. The proposed techniques are exemplified using data from a study of biomarkers associated with coronary heart disease.

Original languageEnglish (US)
Pages (from-to)23-39
Number of pages17
JournalJournal of Nonparametric Statistics
Issue number1
StatePublished - Jan 2010
Externally publishedYes


  • Deconvolution
  • Design of experiments
  • Fourier inversion
  • Nonparametric density estimation
  • Pooling blood samples

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint Dive into the research topics of 'Nonparametric deconvolution of density estimation based on observed sums'. Together they form a unique fingerprint.

Cite this