Journal
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS
Volume 2, Issue 4, Pages 467-476Publisher
WILEY
DOI: 10.1002/wics.97
Keywords
adaptation; asymptotic; indirect data; nonparametric; Oracle; small sample
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Funding
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [0906790] Funding Source: National Science Foundation
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Orthogonal series density estimation is a powerful nonparametric estimation methodology that allows one to analyze and present data at hand without any prior opinion about shape of an underlying density. The idea of construction of an adaptive orthogonal series density estimator is explained on the classical example of a direct sample from a univariate density. Data-driven estimators, which have been used for years, as well as recently proposed procedures, are reviewed. Orthogonal series estimation is also known for its sharp minimax properties which are explained. Furthermore, applications of the orthogonal series methodology to more complicated settings, including censored and biased data as well as estimation of the density of regression errors and the conditional density, are also presented. (C) 2010 John Wiley & Sons, Inc.
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