Abstract
The procedure CUBGCV is an implementation of a recently developed algorithm for fast O(n) calculation of a cubic smoothing spline fitted to n noisy data points, with the degree of smoothing chosen to minimize the expected mean square error at the data points when the variance of the error associated with the data is known, or, to minimize the generalized cross validation (GCV) when the variance of the error associated with the data is unknown. The data may be unequally spaced and nonuniformly weighted. The algorithm exploits the banded structure of the matrices associated with the cubic smoothing spline problem. Bayesian point error estimates are also calculated in O(n) operations.
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