Extreme Value Theory: Limiting Distributions and Applications

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Quantitative Risk Management: Copulas and Generative Adversarial Networks

Explores copulas, simulation algorithms, fitting data with rank correlations, and GANs for image generation.

Extreme Value Theory: GEV and GPD

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

Binary Choice Model

Covers the binary choice model, error term assumptions, specific constants, invariances, and distribution properties.

Model Specification: The Error Term

Delves into the binary choice model, error term specification, and Extreme Value distribution properties.

Sampling Distributions: Theory and Applications

Explores sampling distributions, estimators' properties, and statistical measures for data science applications.

Precipitation and Hydrologic Design

Covers methods to define the design storm, empirical distribution of rainfall maxima, Gumbel distribution, and intensity-duration-frequency relationships.

Generalization Error

Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.

Extreme Value Theory: Maximum Distribution

Explores extreme value theory, focusing on maximum distribution and different types of distributions based on shape parameters.

Threshold Exceedances: Generalized Pareto Distribution

Explores threshold exceedances and the Generalized Pareto Distribution for modeling extreme data points above a specified level.

Maximum Likelihood Estimation

Covers Maximum Likelihood Estimation, focusing on ML Estimation-Distribution, Shrinkage Estimation, and Loss functions.