Smoothing spline

Smoothing spline

The smoothing spline is a method of smoothing, or fitting a smooth curve to a set of noisy observations.


Let (x_i,Y_i); i=1,dots,n be a sequence of observations, modeled by the relation E(Y_i) = mu(x_i). The smoothing spline estimate hatmu of the function mu is defined to be the minimizer (over the class of twice differentiable functions) of
sum_{i=1}^n (Y_i - hatmu(x_i))^2 + lambda int hatmu(x)^2 ,dx.


  1. lambda ge 0 is a smoothing parameter, controlling the trade-off between fidelity to the data and roughness of the function estimate.
  2. The integral is evaluated over the range of the x_i.
  3. As lambdato 0 (no smoothing), the smoothing spline converges to the interpolating spline.
  4. As lambdatoinfty (infinite smoothing), the roughness penalty becomes paramount and the estimate converges to a linear least-squares estimate.
  5. The roughness penalty based on the second derivative is the most common in modern statistics literature, although the method can easily be adapted to penalties based on other derivatives.
  6. In early literature, with equally-spaced x_i, second or third-order differences were used in the penalty, rather than derivatives.
  7. When the sum-of-squares term is replaced by a log-likelihood, the resulting estimate is termed penalized likelihood. The smoothing spline is the special case of penalized likelihood resulting from a Gaussian likelihood.

Derivation of the smoothing spline

It is useful to think of fitting a smoothing spline in two steps:

  1. First, derive the values hatmu(x_i);i=1,ldots,n.
  2. From these values, derive hatmu(x) for all x.

Now, treat the second step first.

Given the vector hat{m} = (hatmu(x_1),ldots,hatmu(x_n))^T of fitted values, the sum-of-squares part of the spline criterion is fixed. It remains only to minimize int hatmu(x)^2 , dx, and the minimizer is a natural cubic spline that interpolates the points (x_i,hatmu(x_i)). This interpolating spline is a linear operator, and can be written in the form

hatmu(x) = sum_{i=1}^n hatmu(x_i) f_i(x) where f_i(x) are a set of spline basis functions. As a result, the roughness penalty has the form
int hatmu''(x)^2 dx = hat{m}^T A hat{m}. where the elements of A are int f_i(x) f_j(x)dx. The basis functions, and hence the matrix A, depend on the configuration of the predictor variables x_i, but not on the responses Y_i or hat m.

Now back the first step. The penalized sum-of-squares can be written as

|Y - hat m|^2 + lambda hat{m}^T A hat m, where Y=(Y_1,ldots,Y_n)^T. Minimizing over hat m gives
hat m = (I + lambda A)^{-1} Y.

Related methods

Smoothing splines are related to, but distinct from:

  • Regression splines. In this method, the data is fitted to a set of spline basis functions with a reduced set of knots, typically by least squares. No roughness penalty is used.
  • Penalized Splines. This combines the reduced knots of regression splines, with the roughness penalty of smoothing splines.

Further reading

  • Wahba, G. (1990). Spline Models for Observational Data. SIAM, Philadelphia.
  • Green, P. J. and Silverman, B. W. (1994). Nonparametric Regression and Generalized Linear Models. CRC Press.


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