Spectral cut-off regularizations for ill-posed linear models
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Publication:2261917
DOI10.3103/S1066530714020033zbMath1308.62016MaRDI QIDQ2261917
Elena Chernousova, Yuri K. Golubev
Publication date: 13 March 2015
Published in: Mathematical Methods of Statistics (Search for Journal in Brave)
minimax riskoracle inequalityspectral cut-off regularizationdata-driven cut-off frequencyill-posed linear model
Linear regression; mixed models (62J05) Bayesian problems; characterization of Bayes procedures (62C10) Minimax procedures in statistical decision theory (62C20) Statistical decision theory (62C99)
Related Items (6)
Adaptivity and Oracle Inequalities in Linear Statistical Inverse Problems: A (Numerical) Survey ⋮ Empirical risk minimization as parameter choice rule for general linear regularization methods ⋮ Multiscale scanning in inverse problems ⋮ Statistical inference for the tangency portfolio in high dimension ⋮ Risk estimators for choosing regularization parameters in ill-posed problems -- properties and limitations ⋮ Higher order moments of the estimated tangency portfolio weights
Cites Work
- Risk hull method and regularization by projections of ill-posed inverse problems
- On universal oracle inequalities related to high-dimensional linear models
- Optimal rates of convergence for nonparametric statistical inverse problems
- Optimal filtering of square-integrable signals in Gaussian noise
- Ordered linear smoothers
- Nonlinear solution of linear inverse problems by wavelet-vaguelette decomposition
- Weak convergence and empirical processes. With applications to statistics
- Neo-classical minimax problems, thresholding and adaptive function estimation
- Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter
- Statistical Inverse Estimation in Hilbert Scales
- Some Comments on C P
- Minimax estimation of the solution of an ill-posed convolution type problem
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