Lectures related to Fit Gumbel Curves to Rainfall Data

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 GEV Parameters

Explores techniques for estimating GEV parameters using graphical and likelihood-based methods, illustrated with real-world examples.

L-Moment Estimation: Probability-Weighted Moments

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

Estimate DDF curves

Discusses intensity-duration-frequency relationships in hydrology and extending theory to events of any duration.

Maximum Likelihood: Inference and Model Comparison

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Extreme Value Theory: Limiting Distributions and Applications

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

Extreme Value Theory: GEV and GPD

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

Maximum Likelihood Inference

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.

Likelihood Ratio Test: Detection & Estimation

Covers the likelihood ratio test for detection and estimation in statistical analysis.

Extreme Value Theory: Maximum Distribution

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

Binary Covariate Impact: 2x2 Contingency Tables

Explores the impact of a binary covariate on binary responses using 2x2 tables.

Maximum Likelihood Estimation: Multivariate Statistics

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

Gaussian Mixture Models: Data Classification

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

Data Classification: Gaussian Mixture Models

Explores Gaussian Mixture Models for data classification, focusing on denoising signals and estimating original data using likelihood and posteriori approaches.

IWLS Algorithm: Overview

Covers the IWLS algorithm for obtaining MLEs in regression models and discusses the likelihood ratio statistic and deviance in model fitting.

Extreme Value Theory: Statistical Applications

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

Likelihood Ratio Test: Hypothesis Testing

Covers the Likelihood Ratio Test and hypothesis testing methods using Maximum Likelihood Estimators.

Venice Sea Levels Analysis

Analyzes Venice sea levels from 1887 to 2017, focusing on maximum levels and trend estimation.

Extreme Region Likelihood Estimation

Covers extreme region likelihood estimation, model complexity, oceanographic data modeling, and threshold likelihood estimation.