FANOK: Knockoffs in Linear Time
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Publication:5154638
DOI10.1137/20M1363698zbMath1475.62109arXiv2006.08790OpenAlexW3186681364MaRDI QIDQ5154638
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Publication date: 5 October 2021
Published in: SIAM Journal on Mathematics of Data Science (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2006.08790
convex optimizationsemidefinite programmingfalse discovery ratecoordinate descentFast Knockoffs (FANOK)
Computational methods for problems pertaining to statistics (62-08) Parametric hypothesis testing (62F03) Semidefinite programming (90C22) Convex programming (90C25) Statistical ranking and selection procedures (62F07)
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- Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
- A well-conditioned estimator for large-dimensional covariance matrices
- Exact post-selection inference, with application to the Lasso
- Conic optimization via operator splitting and homogeneous self-dual embedding
- Valid post-selection inference
- Controlling the false discovery rate via knockoffs
- Hierarchical clustering schemes
- A knockoff filter for high-dimensional selective inference
- Block Coordinate Descent Methods for Semidefinite Programming
- CVXPY: A Python-Embedded Modeling Language for Convex Optimization
- First-Order Methods for Sparse Covariance Selection
- A Note on the Stability of Solving a Rank-p Modification of a Linear System by the Sherman–Morrison–Woodbury Formula
- Fast Estimation of $tr(f(A))$ via Stochastic Lanczos Quadrature
- Practical Sketching Algorithms for Low-Rank Matrix Approximation
- An Interior-Point Method for Semidefinite Programming
- Panning for Gold: ‘Model-X’ Knockoffs for High Dimensional Controlled Variable Selection
- Why Are Big Data Matrices Approximately Low Rank?
- Inferential Theory for Factor Models of Large Dimensions