"Everything should be made as simple as possible, but not simpler." - Albert Einstein

Seminar Announcement

Recent Advances in the Analysis of Massive Spatial Data

Hao Zhang, Department of Statistics, Purdue University

Monday, February 1, 2010

4:00 p.m., 223 Weber

ABSTRACT

The analysis of spatial data collected at a large number of locations and possibly from different sources presents great challenges to statistical inferences. One of the challenges is in dealing with the large covariance matrix and its inverse.  Two dominating approaches to overcoming this large matrix issue are to exploit the computational advantage of sparse matrices and to impose some low rank structure. I will present some recent theoretical and computational developments in both approaches.