Change Point Detection Using Penalized Multidegree Splines

We consider a function estimation method with change point detection using truncated power spline basis and elastic-net-type L1-norm penalty. The penalty controls the jump smoothness depending on value of parameter. In terms proposed estimators, we introduce two computational algorithms for Lagrangian dual problem (coordinate descent algorithm) constrained convex optimization (an algorithm based quadratic programming). Subsequently, investigate relationship between compare them. Using both simulation real data analysis, numerical studies are conducted to validate performance method.