A Sequential Split-Conquer-Combine Approach for Gaussian Process Model in Analysis of Big Spatial Data

摘 要：
 The task of analyzing massive spatial data is extremely challenging. In this talk, we propose a sequential-split-conquer-combine (SSCC) approach for analysis of dependent big spatial data using a Gaussian process model, along with a theoretical support. This SSCC approach can substantially reduce computing time and computer memory requirements. We also show that the SSCC approach is oracle in the sense that the result obtained using the approach is asymptotically equivalent to the one obtained from performing the analysis on the entire data in a super-super computer. A related prediction problem is also considered. The methodology is illustrated numerically using both simulation and a real data example of a computer experiment on modeling room temperatures.  (Joint work with Ying Hung and Chengrui Li)
个人简介：
http://www.stat.rutgers.edu/home/mxie/