swMATH ID: 9371
Software Authors: Wager, Stefan
Description: A geometric approach to density estimation with additive noise.We introduce and study a method for density estimation under an additive noise model. Our method does not attempt to maximize a likelihood, but rather is purely geometric: heuristically, we L 2 -project the observed empirical distribution onto the space of candidate densities that are reachable under the additive noise model. Our estimator reduces to a quadratic program, and so can be computed efficiently. In simulation studies, it roughly matches the accuracy of fully general maximum likelihood estimators at a fraction of the computational cost. We give a theoretical analysis of the estimator and show that it is consistent, attains a quasi-parametric convergence rate under moment conditions, and is robust to model mis-specification. We provide an R implementation of the proposed estimator in the package nlpden.
Homepage: http://www3.stat.sinica.edu.tw/statistica/J24N2/J24N22/J24N22.html
Dependencies: R
Keywords: M-estimators; minimum distance estimators; mixture models; quadratic programming; shape constrained estimators
Related Software: mixfdr; gcrma; Affycomp III; smoothfdr; REBayes; R
Cited in: 3 Publications

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