Some topics in high-dimensional robust inference and graphical modeling
In this dissertation, we focus on large-scale robust inference and high-dimensional graphical modeling. Especially, we study three problems: a large-scale inference method by a tail-robust regression, model specification tests for dependence structure of Gaussian Markov random fields, and a robust Gaussian graph estimation. First of all, we consider the problem of simultaneously testing a large number of general linear hypotheses, encompassing covariate-effect analysis, analysis of variance, and model comparisons. The new challenge that comes along with the overwhelmingly large number of tests ...
(For more, see "View full record.")