Pages that link to "Item:Q1785032"
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The following pages link to Risk estimators for choosing regularization parameters in ill-posed problems -- properties and limitations (Q1785032):
Displaying 15 items.
- Risk hull method and regularization by projections of ill-posed inverse problems (Q449941) (← links)
- Beyond the Bakushinkii veto: regularising linear inverse problems without knowing the noise distribution (Q777510) (← links)
- Early stopping for statistical inverse problems via truncated SVD estimation (Q1616307) (← links)
- Unbiased predictive risk estimation of the Tikhonov regularization parameter: convergence with increasing rank approximations of the singular value decomposition (Q2009116) (← links)
- A probabilistic oracle inequality and quantification of uncertainty of a modified discrepancy principle for statistical inverse problems (Q2153955) (← links)
- Convergence of regularization methods with filter functions for a regularization parameter chosen with GSURE and mildly ill-posed inverse problems (Q2178408) (← links)
- Empirical risk minimization as parameter choice rule for general linear regularization methods (Q2179243) (← links)
- A parameter choice rule for Tikhonov regularization based on predictive risk (Q5113627) (← links)
- Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach Part I: Methodology and Experiments (Q5143322) (← links)
- Lower Risk Bounds and Properties of Confidence Sets for Ill-Posed Estimation Problems with Applications to Spectral Density and Persistence Estimation, Unit Roots, and Estimation of Long Memory Parameters (Q5474987) (← links)
- Predictive risk estimation for the expectation maximization algorithm with Poisson data (Q5859746) (← links)
- Tomographic reconstruction from Poisson distributed data: a fast and convergent EM-TV dual approach (Q6047559) (← links)
- Semi-discrete Tikhonov regularization in RKHS with large randomly distributed noise (Q6050821) (← links)
- Noise Level Free Regularization of General Linear Inverse Problems under Unconstrained White Noise (Q6164171) (← links)
- Risk Estimators for Choosing Regularization Parameters in Ill-Posed Problems - Properties and Limitations (Q6282070) (← links)
