On risk concentration for convex combinations of linear estimators

DOI10.1134/S0032946016040037zbMath1372.62014OpenAlexW2567904107MaRDI QIDQ2364453

Yuri K. Golubev

Publication date: 21 July 2017

Published in: Problems of Information Transmission (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1134/s0032946016040037

zbMATH Keywords

linear modelmaximum likelihood methodwhite Gaussian noiseestimation problemrisk concentrationspectral regularizations

Mathematics Subject Classification ID

Gaussian processes (60G15) Linear regression; mixed models (62J05) Point estimation (62F10) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (3)

Analysis of the Influence of the Lagrange Multiplier on the Operation of the Algorithm for Estimating the Signal Parameters under a Priori Uncertainty ⋮ Noise level estimation in high-dimensional linear models ⋮ On adaptive estimation of linear functionals from observations against white noise

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Concentration inequalities for the exponential weighting method
• On universal oracle inequalities related to high-dimensional linear models
• Ordered linear smoothers
• Mirror descent and nonlinear projected subgradient methods for convex optimization.
• Functional aggregation for nonparametric regression.
• Weak convergence and empirical processes. With applications to statistics
• Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter

This page was built for publication: On risk concentration for convex combinations of linear estimators