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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.

Computer, Electrical and Mathematical Sciences and Engineering (CEMSE)

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