spatio-temporal data analysis

Spatio-Temporal Data Analysis by Transformed Gaussian Processes

Yuan Yan, PhD Student of Prof. Marc Genton, KAUST

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B2 L5 R5220

In the analysis of spatio-temporal data, statistical inference based on the assumption that the data follow a Gaussian distribution is ubiquitous due to its many attractive properties. However, data collected from different fields of science, such as meteorology, astronomy, biology and medicine, rarely meet the assumption of Gaussianity. One option is to apply a monotonic transformation to the data such that the transformed data have a distribution that is close to Gaussian. In this thesis, we focus on a flexible two-parameter family of transformations, the Tukey g-and-h (TGH) transformation

Ruiman Zhong

Ph.D. Student, Statistics
Ruiman is a Ph.D. candidate in Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Paula Moraga in her Geospatial Statistics and Health Surveillance Research Group. Before joining KAUST, Ruiman obtained her Master's Degree in Statistics (August 2020) from Katholieke Universiteit Leuven, Belgium. Her research interests include spatial and spatial-temporal data modeling.

Marc G. Genton

Al-Khawarzmi Distinguished Professor, Statistics
Professor Genton’s pioneering research on large-scale spatial and temporal data has profoundly affected the field of environmental statistics. He is an expert in spatial and spatio-temporal statistics with applications to environmental problems.