2015 Fall

Date: 2015-09-18 Time: 15:30-16:30 Location: BURN 1205 Abstract: In the light of high-dimensional problems, research on the penalized model has received much interest. Correspondingly, several algorithms have been developed for solving penalized high-dimensional models. I will describe fast and efficient unified algorithms for computing the solution path for a collection of penalized models. In particular, we will look at an algorithm for solving L1-penalized learning problems and an algorithm for solving group-lasso learning problems.

Date: 2015-09-11 Time: 15:30-16:30 Location: BURN 1205 Abstract: The estimation of the extremal dependence structure of a multivariate extreme-value distribution is spoiled by the impact of the bias, which increases with the number of observations used for the estimation. Already known in the univariate setting, the bias correction procedure is studied in this talk under the multivariate framework. New families of estimators of the stable tail dependence function are obtained. They are asymptotically unbiased versions of the empirical estimator introduced by Huang (1992).