JSM 2004 - Toronto

Abstract #300800

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Activity Number: 303
Type: Contributed
Date/Time: Wednesday, August 11, 2004 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #300800
Title: State-space Representation for Smoothing Spline ANOVA Models
Author(s): Li Qin*+
Companies: University of Pennsylvania
Address: Department of Biostatistics and Epidemiology, Philadelphia, PA, 19104,
Keywords: high-dimensional smoothing ; Kalman Filter ; smoothing spline ; smoothing spline ANOVA ; state-space model ; tensor product
Abstract:

Smoothing Spline ANOVA (SS-ANOVA) model is an important tool to estimate a multivariate smooth function. The heavy computational demand prevents its applications to high-dimensional problems and large datasets. We propose a state-space representation for the SS-ANOVA. A fast algorithm for multivariate state-space model is adapted for the SS-ANOVA. Representing a SS-ANOVA model in the state space form not only leads to a computationally efficient algorithm for estimation, but also enables the algorithm to be implemented in an online setting which is of particular importance to our real data application. For simplicity, we present our approach for the two-dimensional setting as the extension to higher dimension is straightforward. A numerical example and an application to the EEG data are used as illustration.

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