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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Derives sandwich variance and pairs bootstrap inference for GKRReg coefficients and provides the gkrreg R package with gamma^2 selection tools and datasets.
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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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Statistical Inference for Gaussian Kernel Robust Regression with the gkrreg Package
Derives sandwich variance and pairs bootstrap inference for GKRReg coefficients and provides the gkrreg R package with gamma^2 selection tools and datasets.