In this talk, we first study a set of methods for sparse linear regression including Lasso, OMP, LARS, and ISS. The connections and comparisons of these methods are given, followed by the general framework for sparse linear regression. This framework also motivates new regression methods which give better performance both theoretically and in practice. This is joint/on-going work with Yuan Yao and Wotao Yin.