Variable selection and dimension reduction methods for high dimensional and big-data set

Speaker: Dr Benoit Liquet-Weiland
Affiliation: Macquarie University

Abstract

It is well established that incorporation of prior knowledge on the structure existing in the data for potential grouping of the covariates is key to more accurate prediction and improved interpretability.

In this talk, I will present new multivariate methods incorporating grouping structure in frequentist and Bayesian methodology for variable selection and dimension reduction to tackle the analysis of high dimensional and Big-Data set.  We develop methods using both penalised likelihood methods and Bayesian spike and slab priors to induce structured sparsity. Illustration on genomics dataset will be presented .