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Smoothing spline : ウィキペディア英語版
Smoothing spline

The smoothing spline is a method of fitting a smooth curve to a set of noisy observations using a spline function.
==Definition==
Let (x_i,Y_i);x_1 be a sequence of observations, modeled by the relation Y_i = \mu(x_i). The smoothing spline estimate \hat\mu of the function \mu is defined to be the minimizer (over the class of twice differentiable functions) of
:
\sum_^n (Y_i - \hat\mu(x_i))^2 + \lambda \int_^ \hat\mu''(x)^2 \,dx.

Remarks:
* \lambda \ge 0 is a smoothing parameter, controlling the trade-off between fidelity to the data and roughness of the function estimate.
* The integral is evaluated over the range of the x_i.
* As \lambda\to 0 (no smoothing), the smoothing spline converges to the interpolating spline.
* As \lambda\to\infty (infinite smoothing), the roughness penalty becomes paramount and the estimate converges to a linear least squares estimate.
* 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.
* In early literature, with equally-spaced x_i, second or third-order differences were used in the penalty, rather than derivatives.
* 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.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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