P-splines with derivative based penalties and tensor product smoothing of unevenly distributed data

• Published in: Statistics and computing Vol. 27; no. 4; pp. 985 - 989
• Main Author: Wood, Simon N.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2017; Springer Nature B.V
• Subjects: Artificial Intelligence; Basis functions; Bayesian analysis; Computational efficiency; Computer simulation; Data smoothing; Fines & penalties; Mathematics and Statistics; Probability and Statistics in Computer Science; Sparsity; Splines; Statistical Theory and Methods; Statistics; Statistics and Computing/Statistics Programs; Derivative penalty; Reduced rank spline; P-spline; Tensor product smooth; Smoothing spline
• ISSN: 0960-3174; 1573-1375
• DOI: 10.1007/s11222-016-9666-x

The P-splines of Eilers and Marx (Stat Sci 11:89–121, 1996 ) combine a B-spline basis with a discrete quadratic penalty on the basis coefficients, to produce a reduced rank spline like smoother. P-splines have three properties that make them very popular as reduced rank smoothers: (i) the basis and the penalty are sparse, enabling efficient computation, especially for Bayesian stochastic simulation; (ii) it is possible to flexibly ‘mix-and-match’ the order of B-spline basis and penalty, rather than the order of penalty controlling the order of the basis as in spline smoothing; (iii) it is very easy to set up the B-spline basis functions and penalties. The discrete penalties are somewhat less interpretable in terms of function shape than the traditional derivative based spline penalties, but tend towards penalties proportional to traditional spline penalties in the limit of large basis size. However part of the point of P-splines is not to use a large basis size. In addition the spline basis functions arise from solving functional optimization problems involving derivative based penalties, so moving to discrete penalties for smoothing may not always be desirable. The purpose of this note is to point out that the three properties of basis-penalty sparsity, mix-and-match penalization and ease of setup are readily obtainable with B-splines subject to derivative based penalization. The penalty setup typically requires a few lines of code, rather than the two lines typically required for P-splines, but this one off disadvantage seems to be the only one associated with using derivative based penalties. As an example application, it is shown how basis-penalty sparsity enables efficient computation with tensor product smoothers of scattered data.