Robust Structured Regression via the L2 Criterion
Cross validation for L2E sparse regression with distance penalization
Cross validation for L2E sparse regression with existing penalization ...
Cross validation for L2E trend filtering regression with distance pena...
Cross validation for L2E trend filtering regression with Lasso penaliz...
L2E
L2E convex regression
L2E isotonic regression
L2E multivariate regression
L2E multivariate regression - PG
L2E convex regression - PG
L2E convex regression - MM
L2E isotonic regression - PG
L2E isotonic regression - MM
L2E multivariate regression - MM
L2E sparse regression with distance penalization
L2E sparse regression with existing penalization methods
L2E trend filtering regression with distance penalization
L2E trend filtering regression with Lasso penalization
Solution path of L2E sparse regression with distance penalization
Solution path of L2E sparse regression with existing penalization meth...
Solution path of the L2E trend filtering regression with distance pena...
Solution path of the L2E trend filtering regression with Lasso
Compute kth order differencing matrix
Objective function of the L2E regression - eta
Objective function of the L2E regression - tau
Beta update in L2E convex regression - PG
Beta update in L2E isotonic regression - PG
Beta update in L2E convex regression - MM
Beta update in L2E isotonic regression - MM
Beta update in L2E multivariate regression - MM
Beta update in L2E sparse regression - MM
Beta update in L2E trend filtering regression - MM
Beta update in L2E multivariate regression - PG
Beta update in L2E sparse regression - NCV
Beta update in L2E trend filtering regression using Lasso
Eta update using Newton's method with backtracking
Tau update function
An implementation of a computational framework for performing robust structured regression with the L2 criterion from Chi and Chi (2021+). Improvements using the majorization-minimization (MM) principle from Liu, Chi, and Lange (2022+) added in Version 2.0.