Quantile Regression Analysis of Heterogeneous Data in Ultra-high Dimension

Tuesday, April 5, 2016 - 1:25pm - 2:25pm
Lind 305
Lan Wang (University of Minnesota, Twin Cities)
Modern high-dimensional data are often heterogeneous in the sense that the covariates (predictors) often influence not only the location but also the dispersion or other aspects of the conditional distribution. Quantile regression enjoys some unique advantages for analyzing high dimensional heterogeneous data. By considering different conditional quantiles, we may obtain a more complete picture of the conditional distribution of a response variable given high dimensional covariates. The sparsity level is allowed to be different at different quantile levels. We will discuss recent advances in the statistical theory and algorithms for high-dimensional penalized linear/nonlinear quantile regression. Numerical examples and real data applications will also be presented