A model-agnostic two-stage estimator for conditional quantiles that represents the high-fidelity quantile as a low-fidelity quantile evaluated at a covariate-dependent level, with theory on faster convergence rates under shape similarity.
arXiv preprint arXiv:2211.14578 , year=
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Proposes an adaptive transfer LASSO quantile estimator incorporating source data via penalties, claiming consistency, sparsity, convergence rates, and an algorithm for computation, validated on simulations and protein data.
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Multi-Fidelity Quantile Regression
A model-agnostic two-stage estimator for conditional quantiles that represents the high-fidelity quantile as a low-fidelity quantile evaluated at a covariate-dependent level, with theory on faster convergence rates under shape similarity.
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Transfert learning and adaptive LASSO quantile
Proposes an adaptive transfer LASSO quantile estimator incorporating source data via penalties, claiming consistency, sparsity, convergence rates, and an algorithm for computation, validated on simulations and protein data.