Lectures related to Extreme Value Theory: GEV and GPD

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: Limiting Distributions and Applications

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

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

Maximum Likelihood Estimation: Multivariate Statistics

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

Binary Choice Model

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

Maximum Likelihood Estimation: Properties and Applications

Covers Maximum Likelihood Estimation properties, applications, and assumptions, providing a comprehensive understanding of MLE concepts and their practical implications.

Model Specification: The Error Term MOOC: Introduction to Discrete Choice Models

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

Fisher Information, Cramér-Rao Inequality, MLE

Explains Fisher information, Cramér-Rao inequality, and MLE properties, including invariance and asymptotics.

Statistics for Data Science: Introduction to Statistical Methods

Covers the fundamental concepts of statistics and their application in data science.

Sampling Distributions: Theory and Applications

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

Parameter Estimation

Discusses parameter estimation, including checks, quality, distribution, and statistical properties of estimates.

Maximum Likelihood Estimation

Explores Maximum Likelihood Estimation, covering assumptions, properties, distribution, shrinkage estimation, and loss functions.

Threshold Exceedances: Generalized Pareto Distribution

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

Continuous Random Variables

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

Diffusion Models

Explores diffusion models, focusing on generating samples from a distribution and the importance of denoising in the process.

Statistical Estimation: Properties and Distributions MOOC: Introduction to Discrete Choice Models

Explores statistical parameter estimation, sample accuracy, and Bernoulli variables' properties.

L-Moment Estimation: Probability-Weighted Moments Log in to Mediaspace to watch this video

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

Gaussian Mixture Models: Data Classification Log in to Mediaspace to watch this video

Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.

Extreme-Value Theorems Log in to Mediaspace to watch this video

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

Stability Relationships in Distributions Log in to Mediaspace to watch this video

Explores stability relationships, domains of attraction, and limitations in distributions for statistical modeling and analysis.