Lectures related to Empirical process

Provides an overview of hypothesis testing, p-values, Wald test, and non-parametric statistics.

Covers ECDF, quantiles, mean, median, and box plots for dataset analysis.

Explores the proof and applications of the law of large numbers, emphasizing convergence of the empirical distribution.

Explores statistical analysis, mean square error, and bootstrapping methods in optimization and simulation.

Explores the convergence of empirical mean distributions towards Gaussian distributions, focusing on the Central Limit Theorem.

Covers the analysis of heavy-tailed price returns and clustered volatility in financial data.

Explores confidence intervals, Gaussian estimation, Cramér-Rao inequality, and Maximum Likelihood Estimators.

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

Investigates how birth month influences athlete success, analyzing Japanese athletes' dataset to explore trends in birthdates and occupations.

Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.

Explores nonparametric statistics, covering estimation methods and the bias-variance tradeoff.

Examines levels of analysis, cognitive dissonance theory, empirical examples, literature reviews, and critical thinking in social psychology.

Explores the challenges of multiple hypothesis testing and non-parametric estimation techniques.

Covers hypothesis testing, Q-Q plots, and non-parametric tests in statistics.

Covers the definition of statistics and estimators, examples of estimators, and the method of moments.

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

Covers the Law of Large Numbers in Statistics and methods for deriving estimators and maximum likelihood.

Covers social psychology applied to environmental issues, cognitive dissonance, and empirical examples.

Covers Principal Component Analysis for yield curve shape estimation and dimension reduction in interest rate models.

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