multivariate spatial processes

Mixed graphical-basis models for large nonstationary and multivariate spatial data problems

Dr. William Kleiber, Associate Professor of Applied Mathematics, University of Colorado, USA

Nov 5, 14:00 - 15:00

B1 L4 R4102

multivariate spatial processes statistical climatology and statistics for energy sciences

In this talk, we explore a graphical model representation for the stochastic coefficients relying on the specification of the sparse precision matrix. Sparsity is encouraged in an L1-penalized likelihood framework. Estimation exploits a majorization-minimization approach. The result is a flexible nonstationary spatial model that is adaptable to very large datasets.

Damilya Saduakhas

Ph.D. Student, Statistics

geospatial statistics environmental applications multivariate spatial processes

Damilya Saduakhas is a Ph.D. candidate in the KAUST Stochastic Processes and Applied Statistics Research group (StochProc) under the supervision of Professor David Bolin. Before joining KAUST, Damilya obtained a B.Sc. degree in mathematics from the Nazarbayev University, Kazakhstan. Research Interests Damilya's research interests include geospatial statistics, multivariate spatial processes, environmental modeling, and applications.