Introduces quantile-based effectiveness persistence function as tail mean divided by quantile, shows equivalence to first L-moment of scaled tail, and develops nonparametric estimator with bootstrap equivalence test for biosimilar evaluation.
Regression quantiles,
4 Pith papers cite this work. Polarity classification is still indexing.
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years
2026 4verdicts
UNVERDICTED 4representative citing papers
A penalized likelihood estimator for GEV parameters, weighted by generalized random forest weights, is introduced for extreme quantile regression to improve tail extrapolation and handle many predictors.
Local L1-regularized robust estimators plus server-side robust aggregation achieve near-optimal rates for high-dimensional sparse learning under Byzantine attacks.
Applies conformalized quantile regression with equalized coverage to predict motion control performance in automated vehicles under nominal, degraded, and failed actuator conditions.
citing papers explorer
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Quantile-Based Effectiveness Persistence Function: A Tail-Focused Metric with Theory, Estimation, and Application to Biosimilar Evaluation
Introduces quantile-based effectiveness persistence function as tail mean divided by quantile, shows equivalence to first L-moment of scaled tail, and develops nonparametric estimator with bootstrap equivalence test for biosimilar evaluation.
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Penalized estimation of GEV parameters for extreme quantile regression
A penalized likelihood estimator for GEV parameters, weighted by generalized random forest weights, is introduced for extreme quantile regression to improve tail extrapolation and handle many predictors.
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Byzantine-Robust Distributed Sparse Learning Revisited
Local L1-regularized robust estimators plus server-side robust aggregation achieve near-optimal rates for high-dimensional sparse learning under Byzantine attacks.
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Equalized Coverage in Motion Control Performance Prediction for Self-Adaptive Road Vehicles
Applies conformalized quantile regression with equalized coverage to predict motion control performance in automated vehicles under nominal, degraded, and failed actuator conditions.