This dissertation research addresses how to detect structural changes in linear regression. By introducing a special structure to the design matrix, we convert the structural change detection problem to a variable selection problem. We then design two penalized regression algorithms to accomplish a bi-level structural change detection: they locate the change points and also recognize which predictors contribute to the variation of the model structure.