The Annals of Statistics

Nonparametric estimation by convex programming

Anatoli B. Juditsky and Arkadi S. Nemirovski

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Abstract

The problem we concentrate on is as follows: given (1) a convex compact set X in ℝn, an affine mapping xA(x), a parametric family {pμ(⋅)} of probability densities and (2) N i.i.d. observations of the random variable ω, distributed with the density pA(x)(⋅) for some (unknown) xX, estimate the value gTx of a given linear form at x.

For several families {pμ(⋅)} with no additional assumptions on X and A, we develop computationally efficient estimation routines which are minimax optimal, within an absolute constant factor. We then apply these routines to recovering x itself in the Euclidean norm.

Article information

Source
Ann. Statist., Volume 37, Number 5A (2009), 2278-2300.

Dates
First available in Project Euclid: 15 July 2009

Permanent link to this document
https://projecteuclid.org/euclid.aos/1247663755

Digital Object Identifier
doi:10.1214/08-AOS654

Mathematical Reviews number (MathSciNet)
MR2543692

Zentralblatt MATH identifier
1173.62024

Subjects
Primary: 62G08: Nonparametric regression
Secondary: 62G15: Tolerance and confidence regions 62G07: Density estimation

Keywords
Estimation of linear functional minimax estimation oracle inequalities convex optimization PE tomography

Citation

Juditsky, Anatoli B.; Nemirovski, Arkadi S. Nonparametric estimation by convex programming. Ann. Statist. 37 (2009), no. 5A, 2278--2300. doi:10.1214/08-AOS654. https://projecteuclid.org/euclid.aos/1247663755


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