A0396
Title: Smoothed full-scale approximation of Gaussian process models for computations of large spatial datasets
Authors: Bohai Zhang - University of Wollongong (Australia) [presenting]
Huiyan Sang - Texas A\&M University (United States)
Jianhua Huang - Texas A and M University (United States)
Abstract: Gaussian process (GP) models encounter computational difficulties with large spatial datasets since its computational complexity grows cubically with sample size $n$. Although the Full-Scale Approximation (FSA) using a block modulating function provides an effective way for approximating GP models, it has several shortcomings such as the less smooth prediction surface on block boundaries and sensitiveness to the knot set under small-scale data dependence. To address these issues, we propose a Smoothed Full-Scale Approximation (SFSA) method for the analysis of large spatial dataset. The SFSA leads to a class of scalable GP models, whose covariance functions consist of two parts: A reduced-rank covariance function capturing large-scale spatial dependence and a covariance adjusting local covariance approximation errors of the reduced-rank part both within blocks and between neighboring blocks. The proposed method provides a unified view of approximation methods for GP models, encompassing several existing computational methods for large spatial datasets into one common framework. We illustrate the effectiveness of the SFSA approach through simulation studies and a precipitation dataset.
