Extreme Value Analysis: Applications and Consequences

Related lectures (31)

Page 3 of 4

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

Covers continuous random variables, probability density functions, and distributions, with practical examples.

Covers the statistical analysis of multivariate extremes, including extremal limit theorems and models for extremes of time series.

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

Introduces inferential statistics, covering sampling, central tendency, dispersion, histograms, z-scores, and the normal distribution.

Covers fundamental concepts in probability and statistics, including distributions, properties, and expectations of random variables.

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

Explains how q = rw defines a Poisson process and its intensity.

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Covers the multivariate normal distribution, properties, and sampling methods.