A narrative survey of MIMIC dataset challenges that is undermined by incorrect citations and unsourced performance tables.
Multivariate stable approximation in Wasserstein distance by Stein's method
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abstract
By a delicate analysis for the Stein's equation associated to the $\alpha$-stable law approximation with $\alpha \in (0,2)$, we prove a quantitative stable central limit theorem in Wasserstein type distance, which generalizes the results in the series of work \cite{Xu19, CNX19+,CNXYZ19+} from the univariate case to the multiple variate case. From an explicit computation for Pareto's distribution, we see that the rate of our approximation is sharp. The analysis of the Stein's equation is new and has independent interest.
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Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises
A narrative survey of MIMIC dataset challenges that is undermined by incorrect citations and unsourced performance tables.