期刊
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
卷 11, 期 3, 页码 615-638出版社
TAYLOR & FRANCIS INC
DOI: 10.1198/106186002448
关键词
generalized cross-validation; nonparametric regression; splines
Most existing algorithms for Ruing adaptive splines are based on nonlinear optimization and/or stepwise selection. Stepwise knot selection, although computationally fast, is necessarily suboptimal while determining the best model over the space of adaptive knot splines is a very poorly behaved nonlinear optimization problem. A possible alternative is to use a genetic algorithm to perform knot selection. An adaptive modeling technique referred to as adaptive genetic splines (AGS) is introduced which combines the optimization power of a genetic algorithm with the flexibility of polynomial splines. Preliminary simulation results comparing the performance of AGS to those of existing methods such as HAS, SUREshrink and automatic Bayesian curve fitting are discussed. A real data example involving the application of these methods to a fMRI dataset is presented.
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