About Yan Gong Yan Gong Ph.D. Student, Statistics extreme-value theory Statistics of extremes copulas Bayesian Statistics risk assessment Yan Gong was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Yan successfully defended her PhD thesis entitled " Flexible Multivariate, Spatial, and Causal Models for Extremes" on March 28th, 2023; see her PhD thesis here. Her PhD committee was composed of Professors Raphaël Huser (chair), Valérie Chavez-Demoulin (external examiner from HEC Lausanne at UNIL, Switzerland), David Bolin, and Mohammed-Slim Alouini. For her next career steps, Yan has accepted a short-term postdoctoral research position at Events Presented Events Mar 26 - Apr 1, 2023 Flexible Multivariate, Spatial, and Causal Models for Extremes Yan Gong, Ph.D. Student, Statistics Mar 28, 16:00 - 19:00 B4 L5 R5220 risk assessment statistical analysis Risk assessment for natural hazards and financial extreme events requires the statistical analysis of extreme events, often beyond observed levels. The characterization and extrapolation of the probability of rare events rely on assumptions about the extremal dependence type and about the specific structure of statistical models. In this thesis, we develop models with flexible tail dependence structures, in order to provide a reliable estimation of tail characteristics and risk measures. Our novel methodologies are illustrated by a range of applications to financial, climatic, and health data.
Flexible Multivariate, Spatial, and Causal Models for Extremes Yan Gong, Ph.D. Student, Statistics Mar 28, 16:00 - 19:00 B4 L5 R5220 risk assessment statistical analysis Risk assessment for natural hazards and financial extreme events requires the statistical analysis of extreme events, often beyond observed levels. The characterization and extrapolation of the probability of rare events rely on assumptions about the extremal dependence type and about the specific structure of statistical models. In this thesis, we develop models with flexible tail dependence structures, in order to provide a reliable estimation of tail characteristics and risk measures. Our novel methodologies are illustrated by a range of applications to financial, climatic, and health data.
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