Student Seminars

Seyoung Park
PhD student, Department of Statistics, University of Michigan

Dantzig-type penalization for multiple quantile regression with high dimensional covariates

We study joint quantile regression at multiple quantile levels with high dimensional covariates. Variable selection performed at individual quantile levels may lack stability across neighboring quantiles, making it difficult to understand and interpret the impact of a given covariate on the conditional quantile functions. We propose a Dantzig-type penalization method for sparse model selection at each quantile level which at the same time aiming to shrink the differences of the selected models across neighboring quantiles. We establish an asymptotic property of model selection consistency, and investigate the stability of the selected models across quantiles. The numerical examples and the real data analysis demonstrate that the proposed Dantzig-type quantile regression model selection method provides stable results by reducing the noisy components of usual model selection performed at individual quantile levels.

Student Seminar Archive

For questions regarding the Statistics Student Seminar or if you are interested in presenting, please contact Joonha Park(joonhap@umich.edu) or Jingshen Wang(jshwang@umich.edu).