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NIASRA Seminar Series
December 5, 2017 @ 1:30 pm - 2:30 pm
National Institute for Applied Statistics Research Australia (NIASRA) invites you to attend a seminar at 1:30 pm on Tuesday, 5 December, 2017, at the University of Wollongong. Seminar details follow.
Dr. Bohai Zhang,
Research fellow, Centre for Environmental Informatics, NIASRA, University of Wollongong
Smoothed Full-Scale Approximation of Gaussian Process
Models for Computation of Large Spatial Datasets
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. This method can alleviate the prediction errors on block boundaries; it also leads to more robust inference and prediction results
under different dependence scales due to better approximation of the residual co-variance. We illustrate the effectiveness of the SFSA approach through simulation studies and a total column ozone dataset.
After the seminar, NIASRA will sponsor coffee at The Yard for the audiences (suppose we finish on time). All welcome!