Statistics of Multivariate Extremes: Inference and Models

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Modeling Extremal Processes

Explores extremal limit theorems, Gaussian processes, and modeling rare events in spatial problems and time series.

Poisson Process Approach

Explores the Poisson process approach in extreme value analysis, emphasizing component-wise transformations and likelihood functions for extreme events.

Extreme Value Analysis: Applications and Consequences

Explores extremal limit theorems and statistical analysis for analyzing extreme events like Venezuela rainfall and Venice data.

Poisson Process: Density Theory and Applications

Explores Poisson processes, joint density, independence of events, and likelihood estimation.

Mapping and Colouring: Poisson Processes

Covers the theorems of superposition and colouring for Poisson processes.

Estimating R: Marking and Convergence

Covers the estimation of R in Poisson processes, focusing on marking points and convergence.

Asymptotic Independence Models

Explores extremal limit theorems, statistical analysis, and asymptotic independence models for rare events.

Extreme Value Theory: Return Level Estimation

Explores extremal limit theorems, return level estimation, clustering consequences, and modelling strategies for extreme value theory.

Gaussian Process: Covariance and Correlation Functions

Explores Gaussian processes, covariance functions, intrinsic stationarity, and extreme applications in statistics.

Poisson Processes Theorems

Discusses important theorems related to Poisson processes and their applications in analyzing exceedances and likelihood.