A new kernel-smoothed estimator using complex-domain moment generating functions achieves root-n consistency and asymptotic normality for general linear and nonlinear quantile regression with normal measurement errors in covariates.
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A barrier-enforced multi-objective optimization framework for neural networks generates sharp non-crossing prediction intervals that meet exact target coverage in probabilistic forecasting.
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Quantile regression with measurement errors
A new kernel-smoothed estimator using complex-domain moment generating functions achieves root-n consistency and asymptotic normality for general linear and nonlinear quantile regression with normal measurement errors in covariates.
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Barrier-enforced multi-objective optimization for direct point and sharp interval forecasting
A barrier-enforced multi-objective optimization framework for neural networks generates sharp non-crossing prediction intervals that meet exact target coverage in probabilistic forecasting.