nlpden 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 all top 5 Cited by 7 Authors 1 Huang, Yufen 1 Hwang, J. T. Gene 1 Madrid-Padilla, Oscar-Hernan 1 Pan, Jia-Chiun 1 Polson, Nicholas G. 1 Scott, James G. 1 Wager, Stefan Cited in 3 Serials 1 Computational Statistics and Data Analysis 1 Statistica Sinica 1 Electronic Journal of Statistics Cited in 3 Fields 3 Statistics (62-XX) 1 Numerical analysis (65-XX) 1 Operations research, mathematical programming (90-XX) Citations by Year