Title: Some Remarks on the Bootstrap in High-Dimension
Abstract: The bootstrap (Efron, 79) is a ubiquitous tool in applied statistics. The bootstrap seeks to address the following central question: is it possible to understand the fluctuation behavior of certain functions of the data by appropriately sampling from the observed dataset? (This question is of course important if one wants to create confidence intervals/error bars for functions of the data of interest to the data analyst without relying on asymptotic analysis.) After reviewing some classical results in the situation where one... Read more about CMSA Talk: Noureddine El Karoui
Modeling Ordinal Categorical Data (Day 1)
Harvard Catalyst Biostatistics Seminar
This short course surveys methods for modeling categorical response variables that have a natural ordering of the categories. Such data often occur in the social sciences (e.g., for measuring attitudes and opinions) and in medical and public health disciplines (e.g., pain, quality of life, severity of a condition). Topics to be covered include logistic regression models using cumulative logits with proportional odds structure, non-proportional odds models, other ordinal logistic regression models such as using... Read more about HCatalyst: Alan Agresti
Title: Transelliptical Graphical Models: Theory and Computation
Abstract: We introduce the theory and computation of a semiparametric approach for estimating high dimensional graphical models. There are two main themes of this talk: semiparametric sparsity and statistical optimization. We illustrate the concept of semiparametric sparsity through the transelliptical graphical modeling, and illustrate the concept of statistical optimization through the theoretical analysis of a pathwise coordinate optimization algorithm named PICASA.
(Random Matrix and Probability Theory seminar: flyer)