Extreme Value Theory: Return Level Estimation

Related lectures (32)

Page 2 of 4

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.

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 Extreme Value Theory, focusing on GEV and GPD distributions and the POT Model for threshold exceedances.

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.

Explores the Peaks-Over-Threshold model assumptions, implications, and estimation methods, including fitting a GPD model to exceedances.

Explores advanced graph coloring concepts, including planted coloring, rigidity threshold, and frozen variables in BP fixed points.

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

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

Explores multivariate extremes, including overwhelming sea defenses and heat waves.