More efficient approximation of smoothing splines via space-filling basis selection
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We consider the problem of approximating smoothing spline estimators in a nonparametric regression model. When applied to a sample of size n, the smoothing spline estimator can be expressed as a linear combination of n basis functions, requiring O(n^3) computational time when the number of predictors d≥ 2. Such a sizable computational cost hinders the broad applicability of smoothing splines. In practice, the full sample smoothing spline estimator can be approximated by an estimator based on q randomly-selected basis functions, resulting in a computational cost of O(nq^2). It is known that these two estimators converge at the identical rate when q is of the order O{n^2/(pr+1)}, where p∈ [1,2] depends on the true function η, and r > 1 depends on the type of spline. Such q is called the essential number of basis functions. In this article, we develop a more efficient basis selection method. By selecting the ones corresponding to roughly equal-spaced observations, the proposed method chooses a set of basis functions with a large diversity. The asymptotic analysis shows our proposed smoothing spline estimator can decrease q to roughly O{n^1/(pr+1)}, when d≤ pr+1. Applications on synthetic and real-world datasets show the proposed method leads to a smaller prediction error compared with other basis selection methods.
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