A Bregman-divergence generalization of ELPD enables robust predictive model selection by tuning sensitivity to tail mismatch via a parameter β.
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Introduces convolution smoothing of the check-loss for prediction-powered quantile regression, derives asymptotics under misspecification, and proposes an ensemble estimator.
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Robust Bayesian Predictive Model Selection using Bregman Divergence
A Bregman-divergence generalization of ELPD enables robust predictive model selection by tuning sensitivity to tail mismatch via a parameter β.
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On prediction-powered inference for quantile regression via convolution smoothing
Introduces convolution smoothing of the check-loss for prediction-powered quantile regression, derives asymptotics under misspecification, and proposes an ensemble estimator.