The classical BH, conformal, and competition testing procedures are shown to be special cases of a generalized Randomized BH framework that relaxes exchangeability and enables integration of distributed tests.
A Power and Prediction Analysis for Knockoffs with Lasso Statistics
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
abstract
Knockoffs is a new framework for controlling the false discovery rate (FDR) in multiple hypothesis testing problems involving complex statistical models. While there has been great emphasis on Type-I error control, Type-II errors have been far less studied. In this paper we analyze the false negative rate or, equivalently, the power of a knockoff procedure associated with the Lasso solution path under an i.i.d. Gaussian design, and find that knockoffs asymptotically achieve close to optimal power with respect to an omniscient oracle. Furthermore, we demonstrate that for sparse signals, performing model selection via knockoff filtering achieves nearly ideal prediction errors as compared to a Lasso oracle equipped with full knowledge of the distribution of the unknown regression coefficients. The i.i.d. Gaussian design is adopted to leverage results concerning the empirical distribution of the Lasso estimates, which makes power calculation possible for both knockoff and oracle procedures.
representative citing papers
PRADAS derives a Bayes-optimal mirror statistic for any splitting scheme, establishes asymptotic FDR control under weak dependence, and optimizes the split ratio as a stopping time to improve power over standard equal-split methods.
A stability-derived update formula for a generalized debiased Lasso yields asymptotically accurate approximations for most coordinates under sub-Gaussian designs in the proportional regime, enabling faster resampling-based variable selection.
A high-dimensional Newey-Powell heteroscedasticity test is developed via expectile regression with limiting distribution and asymptotic power obtained through approximate message passing in the n/p to delta regime.
citing papers explorer
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The Randomized BH Procedure: A Generalized Framework Encompassing Conformal and Competition Tests
The classical BH, conformal, and competition testing procedures are shown to be special cases of a generalized Randomized BH framework that relaxes exchangeability and enables integration of distributed tests.
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PRADAS: PRior-Assisted DAta Splitting for False Discovery Rate Control
PRADAS derives a Bayes-optimal mirror statistic for any splitting scheme, establishes asymptotic FDR control under weak dependence, and optimizes the split ratio as a stopping time to improve power over standard equal-split methods.
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Stability of a Generalized Debiased Lasso with Applications to Resampling-Based Variable Selection
A stability-derived update formula for a generalized debiased Lasso yields asymptotically accurate approximations for most coordinates under sub-Gaussian designs in the proportional regime, enabling faster resampling-based variable selection.
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High-dimensional Newey-Powell Test Via Approximate Message Passing
A high-dimensional Newey-Powell heteroscedasticity test is developed via expectile regression with limiting distribution and asymptotic power obtained through approximate message passing in the n/p to delta regime.