Affine estimators minimizing worst-case CVaR of squared error over a type-2 Wasserstein ambiguity set can be exactly computed via tractable SDP when the nominal distribution is finitely supported.
arXiv preprint arXiv:2112.09959 , title =
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A data-driven approach consolidates unstructured disturbances into residual terms estimated from data to yield causal and distributionally consistent stochastic predictors for uncertainty quantification via polynomial chaos expansions and Chebyshev inequalities, validated on Norwegian smart-home实验数据
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Wasserstein Distributionally Robust Risk-Sensitive Estimation via Conditional Value-at-Risk
Affine estimators minimizing worst-case CVaR of squared error over a type-2 Wasserstein ambiguity set can be exactly computed via tractable SDP when the nominal distribution is finitely supported.
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Uncertainty Propagation under Residual Disturbances: A Smart-Home Case Study
A data-driven approach consolidates unstructured disturbances into residual terms estimated from data to yield causal and distributionally consistent stochastic predictors for uncertainty quantification via polynomial chaos expansions and Chebyshev inequalities, validated on Norwegian smart-home实验数据