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Showing 1 to 4 of 4 for “"High Dimensional Estimation"”.
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Spatial Coupling for High-Dimensional Estimation
… for a variety of inference problems. For many high-dimensional regression models with unstructured designs, the Bayes-optimal estimator is computa- tionally intractable. The main idea in spatial coupling is to chain simple, unstructured measurement schemes together to obtain significant gains …
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Modeling and estimation in Gaussian graphical models : maximum-entropy methods and walk-sum analysis
… Gaussian graphical models, namely modeling and estimation. The modeling problem involves learning a sparse graphical model approximation to a specified distribution. The estimation problem in turn exploits this graph structure to solve high-dimensional estimation problems very efficiently. We …
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Bayesian regularization for graphical models and variants: Theory and algorithms
This Dissertation was approved for publication on 2019-04-19 at 09:57.
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New methods for econometric inference
… limit theorem for the maximum of a sum of high dimensional random vectors. Specifically, we establish conditions under which the distribution of the maximum is approximated by that of the maximum of a sum of the Gaussian random vectors with the same covariance matrices as the original …