Bootstrap Testing of the Rank of a Matrix via Least-Squared Constrained Estimation
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Publication:4975340
DOI10.1080/01621459.2013.847841zbMath1367.62043arXiv1301.0768OpenAlexW1975547855MaRDI QIDQ4975340
François Portier, Bernard Delyon
Publication date: 4 August 2017
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1301.0768
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Cites Work
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- Generalized reduced rank tests using the singular value decomposition
- Dimension estimation in sufficient dimension reduction: a unifying approach
- A bootstrap method for assessing the dimension of a general regression problem
- Inference on low-rank data matrices with applications to microarray data
- The asymptotic distribution of singular values with applications to canonical correlations and correspondence analysis
- Inferring the rank of a matrix
- Optimal transformation: a new approach for covering the central subspace
- Corrections to test statistics in principal Hessian directions
- An F Approximation to the Distribution of a Linear Combination of Chi-squared Variables.
- On Directional Regression for Dimension Reduction
- Asymptotic Statistics
- Intentionally Biased Bootstrap Methods
- Extending Sliced Inverse Regression
- Using the Bootstrap to Select One of a New Class of Dimension Reduction Methods
- The estimating function bootstrap
- Sufficient Dimension Reduction via Inverse Regression
- The bootstrap and Edgeworth expansion
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