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.
High-dimensional regression with noisy and missing data: Provable guarantees with nonconvexity
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Develops methodology and asymptotic theory for single and multiple change point recovery in high-dimensional two-directional mean processes, with climate data application.
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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.
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High Dimensional Change Point Models for Two-Directional Data
Develops methodology and asymptotic theory for single and multiple change point recovery in high-dimensional two-directional mean processes, with climate data application.