Point Processes: Extremal Limit Theorems

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

Explores theory and applications of multivariate extremes, emphasizing fitting marginal and dependence models together for accurate estimation.

Covers the application of extreme value theory to point processes and the estimation of extreme events from equally-spaced time series.

Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.

Covers deterministic and stochastic rainfall models in water resources engineering, including generation, calibration, and spatially explicit models.

Explores mapping theorems for Poisson processes and their intensity functions.

Covers the theorems of superposition and colouring for Poisson processes.

Explores point processes in extreme value theory, focusing on modeling exceedances and the theory behind point patterns.

Explores point processes in spatial analysis, focusing on spatial object dissemination and pattern detection.

Explores statistical applications of threshold exceedances and GPD modeling in extreme value theory.