Extreme Statistics: Threshold Models

Related lectures (32)

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Explores Extreme Value Theory applications to time series, discussing extremogram, moving maxima, and rare event threshold sequences.

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

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

Covers the theory of Poisson processes and the estimation of the intensity parameter R.

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

Explores simulation pitfalls, random numbers, discrete & continuous distributions, and Monte-Carlo integration.

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

Delves into Extreme Value Theory, discussing maxima, exceedances, and their statistical implications.

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

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