Student Seminars

 

Chia Chye Yee
PhD student, Department of Statistics, University of Michigan

On the sparse Bayesian Learning of linear models

This work is a re-examination of the sparse Bayesian learning (SBL) of linear regression models of Tipping (2001) in a high-dimensional setting. We propose a hard-thresholded version of the SBL estimator that achieves the non-asymptotic estimation error rate of $\sqrt{s\log p}/\sqrt{n}$, where $n$ is the sample size, $p$ the number of regressors and $s$ the number of non-zero regression coefficients. We also establish that with high-probability the estimator identifies the non-zero regression coefficients. In our simulations we found that sparse Bayesian learning regression performs better than lasso (Tibshiranin 1996) when the signal to be recovered is strong.

 

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).