The Annals of Statistics

Renormalizing Upper and Lower Bounds for Integrated Risk in the White Noise Model:

Mark G. Low

Full-text: Open access

Abstract

Renormalization arguments are used to derive optimal rates of convergence, under integrated squared error loss, for parameter spaces having a certain rectangular structure.

Article information

Source
Ann. Statist., Volume 21, Number 2 (1993), 577-589.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176349137

Digital Object Identifier
doi:10.1214/aos/1176349137

Mathematical Reviews number (MathSciNet)
MR1232505

Zentralblatt MATH identifier
0795.62038

JSTOR
links.jstor.org

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

Keywords
Nonparametric functional estimation renormalization white noise model

Citation

Low, Mark G. Renormalizing Upper and Lower Bounds for Integrated Risk in the White Noise Model:. Ann. Statist. 21 (1993), no. 2, 577--589. doi:10.1214/aos/1176349137. https://projecteuclid.org/euclid.aos/1176349137


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