Lectures related to Extreme Value Estimation

Precipitation and Hydrologic Design

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

Quantitative Risk Management: Copulas and Generative Adversarial Networks

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

Estimating Moments: GEV and GPD

Explores moment estimation in GEV and GPD models, including L-moment estimation and robust parameter estimation.

Extreme Value Theory: GEV and GPD

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

Statistics of Multivariate Extremes

Covers extremal limit theorems, basic statistical analysis, and applications to multivariate extremes, emphasizing the importance of understanding the distribution of maxima.

Extreme Value Theory: Maximum Distribution

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

L-Moment Estimation: Probability-Weighted Moments

Covers L-moment estimation, probability-weighted moments, and maximum likelihood inference basics.

Estimating R: Maximum Likelihood Method

Explores estimating parameters in statistical models using the maximum likelihood method and the method of moments.

Extreme Value Theory: Limiting Distributions and Applications

Covers Extreme Value Theory, GEV distributions, GPD, and threshold exceedances.

Asymptotic Independence Models

Explores asymptotic independence models for multivariate extremes and their applications in extrapolating probabilities to rare events.

Maximum Likelihood Estimation

Covers Maximum Likelihood Estimation in statistical inference, discussing MLE properties, examples, and uniqueness in exponential families.

Extreme Value Analysis: Statistical Consequences

Explores the statistical consequences of extreme value analysis and implications of model mis-specification.

Mixture Models: Simulation-based Estimation

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

Extreme Values: Applications and Probability Framework

Explores extreme values in random variables, applications in environmental factors, reliability modeling, block maxima distribution, and the Generalized Extreme Value distribution.

Logistic Regression: Modeling Binary Response Variables

Explores logistic regression for binary response variables, covering topics such as odds ratio interpretation and model fitting.

Sampling: conditional maximum likelihood estimation

Covers Conditional Maximum Likelihood estimation, contribution to likelihood, and MEV model application in choice-based samples.

Maximum Likelihood Estimation: Properties and Consistency

Explores Maximum Likelihood Estimation properties, consistency, and applications in statistical inference.

Parameter Estimation & Fisher Information

Covers parameter estimation, Fisher information, unbiased estimator, and exponential distributions.

Confidence Intervals: Definition and Estimation

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Extreme-Value Theorems

Explores Extremal Types and Exceedance Theorems, GEV, GPD, stability, and limitations in extreme value modeling.