S3ME recovers sparse causal skeletons in multivariate extremes via proxy-adjusted penalized selection and orients edges by minimizing tail prediction risk under max-linear models, with high-dimensional consistency guarantees.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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stat.ME 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Doubly stable feature selection perturbs the design matrix with increasing additive noise, fits a base selector like Lasso on each perturbed version, and aggregates selection frequencies to identify features stable across both subsampling and design noise levels.
Generalized Rank Regression extends rank methods to non-monotonic scores, derives Bahadur representation and asymptotic normality, proposes a two-stage sub-gradient algorithm, and shows variance equivalence to composite quantile regression.
citing papers explorer
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Causal Discovery in Multivariate Extremes via Tail Asymmetry
S3ME recovers sparse causal skeletons in multivariate extremes via proxy-adjusted penalized selection and orients edges by minimizing tail prediction risk under max-linear models, with high-dimensional consistency guarantees.
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2D Stability Selection: Design Jittering for Doubly Stable Feature Selection
Doubly stable feature selection perturbs the design matrix with increasing additive noise, fits a base selector like Lasso on each perturbed version, and aggregates selection frequencies to identify features stable across both subsampling and design noise levels.
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Generalized Rank Regression
Generalized Rank Regression extends rank methods to non-monotonic scores, derives Bahadur representation and asymptotic normality, proposes a two-stage sub-gradient algorithm, and shows variance equivalence to composite quantile regression.