Smoothing Spline Score Estimation
Copyright policy )Smoothing Spline Score Estimation
Summary: A new characterization and interpretation of the \textit{D. D. Cox} [Ann. Inst. Stat. Math. 37, 271-288 (1985; Zbl 0578.62041)] smoothing spline score estimator is provided, which makes it possible to construct an efficient algorithm for computing this score estimator. On choosing the smoothing parameter, the author proposes adaptive information criteria that outperform conventional data-driven choice criteria based on the assumption of Gaussian innovations. A small Monte Carlo experiment is performed to investigate the finite sample properties of the smoothing spline score estimator as compared to adaptive kernel and weighted kernel score estimators. It is demonstrated that the smoothing spline score estimator is more robust to distributional variation and that all forms of the adaptive information criteria for choosing the smoothing parameter outperform conventional data driven smoothing parameter choice methods based on the Gaussian innovations assumption.
finite sample properties, smoothing spline score estimator, smoothing parameter, adaptive kernel, Probabilistic methods, stochastic differential equations, Gaussian innovations, robust model selection, Numerical computation using splines, Density estimation, Computational methods for sparse matrices, adaptive information criteria, nonparametric regression, efficient algorithm, banded matrices, Numerical smoothing, curve fitting, weighted kernel score estimators, Monte Carlo
finite sample properties, smoothing spline score estimator, smoothing parameter, adaptive kernel, Probabilistic methods, stochastic differential equations, Gaussian innovations, robust model selection, Numerical computation using splines, Density estimation, Computational methods for sparse matrices, adaptive information criteria, nonparametric regression, efficient algorithm, banded matrices, Numerical smoothing, curve fitting, weighted kernel score estimators, Monte Carlo
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