A multiple testing approach to the regularisation of large sample correlation matrices
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Publication:1739875
DOI10.1016/j.jeconom.2018.10.006zbMath1452.62365OpenAlexW3125902100WikidataQ128998118 ScholiaQ128998118MaRDI QIDQ1739875
Natalia Bailey, L. Vanessa Smith, M. Hashem Pesaran
Publication date: 29 April 2019
Published in: Journal of Econometrics (Search for Journal in Brave)
Full work available at URL: https://eprints.whiterose.ac.uk/138089/1/BPS_14_October_2018.pdf
Computational methods for problems pertaining to statistics (62-08) Estimation in multivariate analysis (62H12) Paired and multiple comparisons; multiple testing (62J15)
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Uses Software
Cites Work
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- The Adaptive Lasso and Its Oracle Properties
- A well-conditioned estimator for large-dimensional covariance matrices
- High dimensional covariance matrix estimation using a factor model
- Covariance estimation: the GLM and regularization perspectives
- High-dimensional covariance matrix estimation in approximate factor models
- Covariance regularization by thresholding
- Operator norm consistent estimation of large-dimensional sparse covariance matrices
- On the validity of the formal Edgeworth expansion
- Some theory for Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations
- A new perspective on robust \(M\)-estimation: finite sample theory and applications to dependence-adjusted multiple testing
- Sparse permutation invariant covariance estimation
- Design-free estimation of variance matrices
- Regularized estimation of large covariance matrices
- Adapting to unknown sparsity by controlling the false discovery rate
- A new approach to Cholesky-based covariance regularization in high dimensions
- High-dimensional volatility matrix estimation via wavelets and thresholding
- Positive definite estimators of large covariance matrices
- Shrinkage Estimators for Covariance Matrices
- Variances Are Not Always Nuisance Parameters
- Adaptive Thresholding for Sparse Covariance Matrix Estimation
- Model selection and estimation in the Gaussian graphical model
- First-Order Methods for Sparse Covariance Selection
- FORMALIZED DATA SNOOPING BASED ON GENERALIZED ERROR RATES
- The econometrics of mean‐variance efficiency tests: a survey
- Matrix Analysis
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Maximum likelihood estimation of generalised linear models for multivariate normal covariance matrix
- Nonconjugate Bayesian Estimation of Covariance Matrices and Its Use in Hierarchical Models
- A Hierarchical Eigenmodel for Pooled Covariance Estimation
- Robustness and Accuracy of Methods for High Dimensional Data Analysis Based on Student’s t-Statistic
- Positive-Definite ℓ1-Penalized Estimation of Large Covariance Matrices
- Joint mean-covariance models with applications to longitudinal data: unconstrained parameterisation
- Tuning-parameter selection in regularized estimations of large covariance matrices
- Partial Correlation Estimation by Joint Sparse Regression Models
- Generalized Thresholding of Large Covariance Matrices
- Exact and Approximate Stepdown Methods for Multiple Hypothesis Testing
- THE FREQUENCY DISTRIBUTION OF THE PRODUCT-MOMENT CORRELATION COEFFICIENT IN RANDOM SAMPLES OF ANY SIZE DRAWN FROM NON-NORMAL UNIVERSES
- Testing Weak Cross-Sectional Dependence in Large Panels
- The bootstrap and Edgeworth expansion
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