The Annals of Statistics

Renormalization and White Noise Approximation for Nonparametric Functional Estimation Problems

Mark G. Low

Full-text: Open access

Abstract

White noise models often renormalize exactly yielding optimal rates of convergence for pointwise nonparametric functional estimation problems. Similar rescaling ideas lead to a sequence of experiments appropriate for pointwise density estimation problems.

Article information

Source
Ann. Statist., Volume 20, Number 1 (1992), 545-554.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176348538

Digital Object Identifier
doi:10.1214/aos/1176348538

Mathematical Reviews number (MathSciNet)
MR1150360

Zentralblatt MATH identifier
0756.62018

JSTOR
links.jstor.org

Subjects
Primary: 62G07: Density estimation
Secondary: 62C20: Minimax procedures

Keywords
Nonparametric functional estimation renormalization white noise approximation density estimation

Citation

Low, Mark G. Renormalization and White Noise Approximation for Nonparametric Functional Estimation Problems. Ann. Statist. 20 (1992), no. 1, 545--554. doi:10.1214/aos/1176348538. https://projecteuclid.org/euclid.aos/1176348538


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