statistical analysis

Statistical Models and Methods Based on Stochastic Partial Differential Equations

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This talk presents an overview of our research on statistical methods using stochastic partial differential equations (SPDEs), focusing on non-Gaussian random fields and fractional-order SPDEs, and theory for random fields and statistical analysis on metric graphs, highlighting theoretical contributions, software development, and applications relevant to KAUST RDI pillars.

Statistical Analysis of Multi-day Solar Irradiance using a Threshold Time Series model

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B9 H2 R2325

Abstract The analysis of solar irradiance has important applications in predicting solar energy production from solar power plants. Although the sun provides every day more energy than we need, the variability caused by environmental conditions affects electricity production. Most of the existing statistical models to forecast solar irradiance are linear and highly depend on normality assumptions. However, solar irradiance shows strong non-linearity and is only measured during the day time. Thus, we propose a new multi-day threshold autoregressive (TAR) model to quantify the variability of the

Flexible Multivariate, Spatial, and Causal Models for Extremes

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B4 L5 R5220

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.