Adaptive Order Selection for Spline Smoothing
Randy Eubank, (Texas A&M University), firstname.lastname@example.org,
Chunfeng Huang , (North Dakota State University), email@example.com, and
Suojin Wang, (Texas A&M University), firstname.lastname@example.org
Computational methods are presented for spline smoothing that make it practical to compute smoothing splines of degrees other than just the standard cubic case. Specifically, an order n algorithm is developed that has conceptual and practical advantages relative to classical methods. From a conceptual standpoint, the algorithm uses only standard programming techniques that do not require specialized knowledge about spline functions, methods for solving sparse equation systems or Kalman filtering. This allows for the practical development of methods for adaptive selection of both the level of smoothing and degree of the estimator. Simulation experiments are presented that show adaptive degree selection can improve estimator efficiency over the use of cubic smoothing splines. Run time comparisons are also conducted between the proposed algorithm and a classical, band-limited, computing method for the cubic case.