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To consider model uncertainty in global Fr\'{e}chet regression and improvedensity response prediction, we propose a frequentist model averaging method.The weights are chosen by minimizing a cross-validation criterion based onWasserstein distance. In the cases where all candidate models are misspecified,we prove that the corresponding model averaging estimator has asymptoticoptimality, achieving the lowest possible Wasserstein distance. When there arecorrectly specified candidate models, we prove that our method asymptoticallyassigns all weights to the correctly specified models. Numerical results ofextensive simulations and a real data analysis on intracerebral hemorrhage datastrongly favour our method.

Frequentist Model Averaging for Global Fr\'{e}chet Regression