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Explores non-parametric estimation using kernel density estimators to estimate distribution functions and parameters, emphasizing bandwidth selection for optimal accuracy.
Delves into instrumental variable approaches for optimal treatment regimes, including precision medicine and personalized recommendations, with a focus on identification conditions and robust classification-based estimators.
Explores the application of statistical physics in computational problems, covering topics such as Bayesian inference, mean-field spin glass models, and compressed sensing.
