Lectures related to Asymptotic Independence Models

Mixture Models: Simulation-based Estimation

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

Estimation and Confidence Intervals

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Mixture models: taste heterogeneity MOOC: Selected Topics on Discrete Choice

Explores mixture models in discrete choice and random parameters estimation results.

Testing: t-tests

Covers t-tests, p-values calculation, and comparison of coefficients.

Parameter Estimation

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

Copulas and Tail Dependence

Explores copulas, rank correlations, and tail dependence measures in risk management.

Estimation Methods in Probability and Statistics

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Confidence Intervals: Definition and Estimation

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

Bayesian Parameter Estimation

Covers an example of Bayesian parameter estimation and the trade-off between bias and variance in supervised learning.

Personalized Menu Optimization

Explores Bayesian methods in choice modeling for personalized menu optimization and individual choice prediction.

Generative Models: Logistic Regression & Gaussian Distribution

Explores generative models, logistic regression, and Gaussian distribution for approximating posterior probabilities and optimizing model performance.

Intro to Quantum Sensing: Parameter Estimation and Fisher Information

Introduces Fisher Information for parameter estimation based on collected data.

Sampling Theory: Sufficiency and Ancillarity

Explores sufficiency and ancillarity in sampling theory, emphasizing the importance of sufficient statistics in compressing data without losing information.

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

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

Maximum Likelihood Inference

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

Statistics for Data Science: Introduction to Statistical Methods

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

Bias, Variance, Consistency, EMV

Covers bias, variance, mean squared error, consistency, and maximum likelihood estimation in the Poisson model.

Panel data: serial correlation MOOC: Selected Topics on Discrete Choice

Explores the panel effect model with fixed and random effects, discussing estimation challenges and the impact of serial correlation.

Statistics of Multivariate Extremes Log in to Mediaspace to watch this video

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

Extreme Value Theory: Point Processes Log in to Mediaspace to watch this video

Covers the application of extreme value theory to point processes and the estimation of extreme events from equally-spaced time series.