Introduces convolution smoothing of the check-loss for prediction-powered quantile regression, derives asymptotics under misspecification, and proposes an ensemble estimator.
year 2005
2 Pith papers cite this work. Polarity classification is still indexing.
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A high-dimensional Newey-Powell heteroscedasticity test is developed via expectile regression with limiting distribution and asymptotic power obtained through approximate message passing in the n/p to delta regime.
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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.
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High-dimensional Newey-Powell Test Via Approximate Message Passing
A high-dimensional Newey-Powell heteroscedasticity test is developed via expectile regression with limiting distribution and asymptotic power obtained through approximate message passing in the n/p to delta regime.