Effective Two-Stage Estimation for a Linear Function of High-Dimensional Gaussian Means
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Publication:3068084
DOI10.1080/07474946.2010.520630zbMath1203.62108OpenAlexW2087900947MaRDI QIDQ3068084
Publication date: 13 January 2011
Published in: Sequential Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/07474946.2010.520630
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Related Items (6)
Correlation tests for high-dimensional data using extended cross-data-matrix methodology ⋮ Inference on high-dimensional mean vectors with fewer observations than the dimension ⋮ Unnamed Item ⋮ Discussion on “Two-Stage Procedures for High-Dimensional Data” by Makoto Aoshima and Kazuyoshi Yata ⋮ Two-Stage Procedures for High-Dimensional Data ⋮ Authors' Response
Cites Work
- Asymptotic second-order consistency for two-stage estimation methodologies and its applications
- Double shrink methodologies to determine the sample size via covariance structures
- Second-order properties of a two-stage fixed-size confidence region for the mean vector of a multivariate normal distribution
- On sequential fixed-size confidence regions for the mean vector
- On a two-stage procedure having second-order properties with applications
- Asymptotic Second-Order Efficiency of a Two-Stage Procedure for Estimating a Linear Function of Normal Means
- Asymptotic Second-Order Efficiency for Multivariate Two-Stage Estimation of a Linear Function of Normal Mean Vectors
- Two-Stage Equivalence Tests That Control Both Size and Power
- Intrinsic Dimensionality Estimation of High-Dimension, Low Sample Size Data withD-Asymptotics
- PCA Consistency for Non-Gaussian Data in High Dimension, Low Sample Size Context
- TWO-STAGE ESTIMATION OF A LINEAR FUNCTION OF NORMAL MEANS WITH SECOND-ORDER APPROXIMATIONS
- A two-stage procedure for estimating an linear function of \(k\) multinormal mean vectors when covariance matrices are unknown
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