A three-manuscript thesis: nonlinear MI-based screening for neuroimaging, faster accelerated-gradient hyperparameters for nonconvex sparse learning, and a theoretically derived but unvalidated qGaussian linear mixed model.
URL http://www.jstor.org/stable/3085904
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Contributions to Robust and Efficient Methods for Analysis of High Dimensional Data
A three-manuscript thesis: nonlinear MI-based screening for neuroimaging, faster accelerated-gradient hyperparameters for nonconvex sparse learning, and a theoretically derived but unvalidated qGaussian linear mixed model.