Constrained log-optimal e-variables are obtained by post-processing the unconstrained optimal e-variable via an appropriate transformation.
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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.
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Optimal e-variables under constraints
Constrained log-optimal e-variables are obtained by post-processing the unconstrained optimal e-variable via an appropriate transformation.
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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.