Model selection and sharp asymptotic minimaxity

DOI10.1007/s00440-012-0424-5OpenAlexW1983362045MaRDI QIDQ1955840

Harrison H. Zhou, Zheyang Wu

Publication date: 19 June 2013

Published in: Probability Theory and Related Fields (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00440-012-0424-5

zbMATH Keywords

waveletsmodel selectionthresholdingmultiple comparisonsminimax estimationFDRwavelet denoisingsmoothing parameter selectionsharp asymptotic minimaxity

Mathematics Subject Classification ID

Nonparametric inference (62Gxx)

Related Items (4)

SLOPE is adaptive to unknown sparsity and asymptotically minimax ⋮ Optimal false discovery control of minimax estimators ⋮ SLOPE-adaptive variable selection via convex optimization ⋮ Sparse model selection under heterogeneous noise: exact penalisation and data-driven thresholding

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Asymptotic equivalence of spectral density estimation and Gaussian white noise
• Robust nonparametric estimation via wavelet median regression
• Nonparametric regression in exponential families
• A simple forward selection procedure based on false discovery rate control
• Asymptotic equivalence and adaptive estimation for robust nonparametric regression
• Risk bounds for model selection via penalization
• Minimax risk over \(l_ p\)-balls for \(l_ q\)-error
• Asymptotic equivalence of nonparametric regression and white noise
• Asymptotic equivalence of density estimation and Gaussian white noise
• Robbins, empirical Bayes and microarrays
• Minimax estimation via wavelet shrinkage
• The risk inflation criterion for multiple regression
• Minimal penalties for Gaussian model selection
• On optimality of Bayesian testimation in the normal means problem
• Adapting to unknown sparsity by controlling the false discovery rate
• Calibration and empirical Bayes variable selection
• Adapting to Unknown Smoothness via Wavelet Shrinkage
• Central limit theorems for sums of extreme values
• The Covariance Inflation Criterion for Adaptive Model Selection
• Ideal spatial adaptation by wavelet shrinkage
• Memory-universal prediction of stationary random processes
• Order Statistics
• Local asymptotic coding and the minimum description length
• Gaussian model selection
• A new look at the statistical model identification

This page was built for publication: Model selection and sharp asymptotic minimaxity