Statistics Seminar: Liquet-Weiland -- Variable Selection and Dimension Reduction methods for high dimensional and Big-Data Set

Variable Selection and Dimension Reduction methods for high dimensional and Big-Data Set 

Time: 14:00 - 15:00 29 April 2022
Location: AGR 829, or Zoom at https://uni-sydney.zoom.us/j/84980973940


Speaker: Prof.  Benoit Liquet-Weiland (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.