Discussion on “Two-Stage Procedures for High-Dimensional Data” by Makoto Aoshima and Kazuyoshi Yata

DOI10.1080/07474946.2011.619092zbMath1284.62503OpenAlexW1965126862MaRDI QIDQ5894437

Tze Leung Lai, Ching-Kang Ing

Publication date: 28 December 2011

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

Full work available at URL: https://doi.org/10.1080/07474946.2011.619092

zbMATH Keywords

sparsitysure screening propertytwo-stage procedureorthogonal greedy algorithmfixed-width confidence intervalhigh-dimensional information criterion

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Parametric tolerance and confidence regions (62F25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Hypothesis testing in multivariate analysis (62H15) Central limit and other weak theorems (60F05) Sequential statistical analysis (62L10)

Cites Work

• Least angle regression. (With discussion)
• Weak greedy algorithms
• Boosting for high-dimensional linear models
• Extended Bayesian information criteria for model selection with large model spaces
• Greed is Good: Algorithmic Results for Sparse Approximation
• Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
• Ideal spatial adaptation by wavelet shrinkage
• Boosting With theL₂Loss
• Regularization Parameter Selections via Generalized Information Criterion
• On the Asymptotic Theory of Fixed-Width Sequential Confidence Intervals for the Mean

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