Abstract
A new characterization and interpretation of the Cox [Ann. Instit. Statist. Math., 37 (1985), pp. 271–288] 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 innovation. 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 innovation assumption.
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