Adaptive discretization for signal detection in statistical inverse problems

DOI10.1080/00036811.2014.900662zbMath1310.62058OpenAlexW2023372004WikidataQ58259705 ScholiaQ58259705MaRDI QIDQ4982027

Peter Mathé

Publication date: 23 March 2015

Published in: Applicable Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00036811.2014.900662

zbMATH Keywords

inverse problemsprojection schemeinverse testlinear regularization

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Signal detection and filtering (aspects of stochastic processes) (60G35)

Cites Work

• Oracle-type posterior contraction rates in Bayesian inverse problems
• Minimax signal detection in ill-posed inverse problems
• Inequalities for the Schatten p-Norm. IV
• Non asymptotic minimax rates of testing in signal detection with heterogeneous variances
• Discrepancy principle for statistical inverse problems with application to conjugate gradient iteration
• Regularization of some linear ill-posed problems with discretized random noisy data
• Discretization strategy for linear ill-posed problems in variable Hilbert scales
• Convergence Rates of General Regularization Methods for Statistical Inverse Problems and Applications
• Learning Bounds for Kernel Regression Using Effective Data Dimensionality

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