Seminars

Abstract In many observational studies, researchers are interested in studying the effects of multiple exposures on the same outcome. Unmeasured…

Abstract Deep Neural Networks (DNNs) have performed outstandingly in several computer vision tasks. However, the performance of DNNs has always…

Abstract There has been tremendous recent progress in the development of prediction models, but statistical inference on parameters of interest…

Abstract Unmeasured confounding is the central complication of causal inference resulting from unknown common operating mechanism over both uncontrolled treatment…

Abstract Gaussian graphical models are a powerful tool to learn the conditional independence structure in multivariate data with inference often…

Abstract Empirical likelihood-based inference incorporates the advantages of both parametric and nonparametric modeling by allowing inference on parameters of the…

Abstract Local field potentials (LFPs) are signals that measure electrical activity in localized cortical regions from implanted tetrodes in the…

Abstract The quantification of microbiome using next-generation sequencing technologies has sparked intense interest in the study of the human microbiome…

Abstract High dimensional distributions, especially those with heavy tails, are notoriously difficult for off-the-shelf MCMC samplers: the combination of unbounded…

Abstract Qualitative descriptions of the shape of a function (e.g., convex, monotone, bimodal) and their use in assessing methodological tools…