Solution Paths for the Generalized Lasso with Applications to Spatially Varying Coefficients Regression

摘要

Penalized regression can improve prediction accuracy and reduce dimension. The generalized lasso problem is used in many applications in various fields. The generalized lasso penalizes a linear transformation of the coefficients rather than the coefficients themselves. Here we propose an algorithm to solve the generalized lasso problem and provide the full solution path. A confidence set can then be constructed on the generalized lasso parameters based on the modified residual bootstrap lasso. We demonstrate our approach using spatially varying coefficients regression. We show that our algorithm is both accurate and efficient compared to previous work.