Model selection for estimating the non zero components of a Gaussian vector

DOI10.1051/ps:2006004zbMath1187.62103OpenAlexW2052099002MaRDI QIDQ5429572

Sylvie Huet

Publication date: 30 November 2007

Published in: ESAIM: Probability and Statistics (Search for Journal in Brave)

Full work available at URL: http://www.numdam.org/item?id=PS_2006__10__164_0

zbMATH Keywords

model selectionKullback riskpenalised likelihood criteria

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Nonparametric estimation (62G05) Nonparametric statistical resampling methods (62G09)

Related Items (2)

Local comparison of empirical distributions via nonparametric regression ⋮ Gaussian model selection with an unknown variance

Uses Software

• EBayesThresh

Cites Work

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• A Bayesian analysis of the minimum AIC procedure
• Estimating the dimension of a model
• Risk bounds for model selection via penalization
• Maximum likelihood principle and model selection when the true model is unspecified
• Adaptive estimation of a quadratic functional by model selection.
• Adapting to unknown sparsity by controlling the false discovery rate
• Empirical Bayes selection of wavelet thresholds
• Universal coding, information, prediction, and estimation
• An Analysis for Unreplicated Fractional Factorials
• Regression and time series model selection in small samples
• Model selection using wavelet decomposition and applications
• Adaptive tests of qualitative hypotheses
• Inference for Effect-Saturated Fractional Factorials
• Gaussian model selection

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