A simple smoothing spline, III
Copyright policy )A simple smoothing spline, III
This paper is the third in a series devoted to the study of linear smoothing splines; for the review of Part II see [Zbl 1057.62515]. The present article focuses on computational aspects with the specific goal of developing an in-depth understanding of the methods for computing the linear smoothing spline. Comparisons are made between two efficient methods for computing the estimator using band-limited basis and the Kalman filter (KF). Particularly, KF is shown to be an efficient method for computing under the Kimeldorf-Wahba representation for the estimator. The run time comparisons are made between band-limited B-spline and Kalman filter based algorithms.
- The University of Texas System United States
- Texas A&M University United States
Kernel approximation, Boundary bias, cross-validation, Numerical computation using splines, Inference from stochastic processes and prediction, band-limited B-spline, Linear inference, regression, Asymptotic properties of nonparametric inference, Mean squared error, linear smoothing spline, Kalman filter, Computational methods for problems pertaining to statistics
Kernel approximation, Boundary bias, cross-validation, Numerical computation using splines, Inference from stochastic processes and prediction, band-limited B-spline, Linear inference, regression, Asymptotic properties of nonparametric inference, Mean squared error, linear smoothing spline, Kalman filter, Computational methods for problems pertaining to statistics
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