Special Colloquium
Xiaoxiao Sun
University of Georgia
Theory Informs Practice: Smoothing Parameters Selection for Smoothing Spline ANOVA Models in Large Samples
Abstract: Large samples have been generated routinely from various sources. Classic statistical models, such as
smoothing spline ANOVA models, are not well equipped to analyze such large samples due to expensive
computational costs. In particular, the daunting computational costs of selecting smoothing parameters
render the smoothing spline ANOVA models impractical. In this talk, I will present an asympirical
(asymptotic + empirical) smoothing parameters selection approach for smoothing spline ANOVA models
in large samples. The proposed method can significantly reduce computational costs of selecting
smoothing parameters in high-dimensional and large-scale data. We show smoothing parameters
chosen by the proposed method tend to the optimal smoothing parameters minimizing a risk function.
In addition, the estimator based on the proposed smoothing parameters achieves the optimal
convergence rate. Extensive simulation studies will be presented to demonstrate numerical advantages
of our method over competing methods. I will further illustrate the empirical performance of the
proposed approach using real data.
Wednesday January 17, 2018 at 3:00 PM in SEO 636