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Penalized splines, or P-splines, are regression splines fit by least-squares with a roughness penaly. P-splines have much in common with smoothing splines, but the type of penalty used with a P-spline is somewhat more general than for a smoothing spline. Also, the number and location of the knots of a P-spline is not fixed as with a smoothing spline. Generally, the knots of a P-spline are at fixed quantiles of the independent variable and the only tuning parameter to choose is the number of knots. In this article, the effects of the number of knots on the performance of P-splines are studied. Two algorithms are proposed for the automatic selection of the number of knots. The myoptic algorithm stops when no improvement in the generalized cross validation statistic (GCV) is noticed with the last increase in the number of knots. The full search examines all candidates in a fixed sequence of possible numbers of knots and chooses the candidate that minimizes GCV. The myoptic algo...
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David Ruppert (Sun,) studied this question.
synapsesocial.com/papers/6a2154495c0c8498e257e6fd — DOI: https://doi.org/10.1198/106186002853
David Ruppert
Cornell University
Journal of Computational and Graphical Statistics
Cornell University
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