Optimal homogeneous extreme predictors are non-extreme conditional quantiles of tilted distributions from the angular measure, with universally consistent peaks-over-threshold estimators.
Bayesian semiparametric modelling in quantile regression
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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A new Bayesian effect selection method for additive quantile regression separates linear and nonlinear components via Demmler-Reinsch basis and spike-slab priors, demonstrated on NO2 pollution data.
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On the optimal prediction of extreme events
Optimal homogeneous extreme predictors are non-extreme conditional quantiles of tilted distributions from the angular measure, with universally consistent peaks-over-threshold estimators.
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Bayesian Effect Selection for Additive Quantile Regression with an Application to Air Pollution Thresholds
A new Bayesian effect selection method for additive quantile regression separates linear and nonlinear components via Demmler-Reinsch basis and spike-slab priors, demonstrated on NO2 pollution data.