Modeling Extremal Processes

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Assumption-lean Inference: Generalised Linear Model Parameters

Explores assumption-lean inference for statistical estimands in generalised linear models, emphasizing robust and generic approaches.

Stochastic Models for Communications

Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.

Statistical Analysis of Multivariate Extremes

Covers the statistical analysis of multivariate extremes, including extremal limit theorems and models for extremes of time series.

Eigenstate Thermalization Hypothesis

Explores the Eigenstate Thermalization Hypothesis in quantum systems, emphasizing the random matrix theory and the behavior of observables in thermal equilibrium.

Statistical Inference: Linear Models

Explores statistical inference for linear models, covering model fitting, parameter estimation, and variance decomposition.

Probabilities and Statistics: Key Theorems and Applications

Discusses key statistical concepts, including sampling dangers, inequalities, and the Central Limit Theorem, with practical examples and applications.

Statistical Inference: Approximate Critical Values and Confidence Intervals

Covers the construction of confidence intervals and approximate critical values in statistical inference.

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Asymptotic Independence Models

Explores extremal limit theorems, statistical analysis, and asymptotic independence models for rare events.

Decision Theory: Risk and Inference

Explores decision theory, risk functions, and inference in statistical analysis.