The Annals of Statistics
- Ann. Statist.
- Volume 41, Number 5 (2013), 2608-2638.
Local and global asymptotic inference in smoothing spline models
This article studies local and global inference for smoothing spline estimation in a unified asymptotic framework. We first introduce a new technical tool called functional Bahadur representation, which significantly generalizes the traditional Bahadur representation in parametric models, that is, Bahadur [Ann. Inst. Statist. Math. 37 (1966) 577–580]. Equipped with this tool, we develop four interconnected procedures for inference: (i) pointwise confidence interval; (ii) local likelihood ratio testing; (iii) simultaneous confidence band; (iv) global likelihood ratio testing. In particular, our confidence intervals are proved to be asymptotically valid at any point in the support, and they are shorter on average than the Bayesian confidence intervals proposed by Wahba [J. R. Stat. Soc. Ser. B Stat. Methodol. 45 (1983) 133–150] and Nychka [J. Amer. Statist. Assoc. 83 (1988) 1134–1143]. We also discuss a version of the Wilks phenomenon arising from local/global likelihood ratio testing. It is also worth noting that our simultaneous confidence bands are the first ones applicable to general quasi-likelihood models. Furthermore, issues relating to optimality and efficiency are carefully addressed. As a by-product, we discover a surprising relationship between periodic and nonperiodic smoothing splines in terms of inference.
Ann. Statist., Volume 41, Number 5 (2013), 2608-2638.
First available in Project Euclid: 19 November 2013
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Shang, Zuofeng; Cheng, Guang. Local and global asymptotic inference in smoothing spline models. Ann. Statist. 41 (2013), no. 5, 2608--2638. doi:10.1214/13-AOS1164. https://projecteuclid.org/euclid.aos/1384871347
- Supplementary material: Supplement to “Local and global asymptotic inference in smoothing spline models”. The supplementary materials contain all the proofs of the theoretical results in the present paper.