Asymptotic theory (statistics)

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Related lectures (32)

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Confidence Intervals and MLE Limit Theorems

Explores constructing confidence intervals and MLE limit theorems for large samples.

Shrinkage Estimation of Large Covariance Matrices

Explores shrinkage estimation of high-dimensional covariance matrices, comparing linear and nonlinear approaches for improved accuracy.

Statistical Theory: Inference and Optimality

Explores constructing confidence regions, inverting hypothesis tests, and the pivotal method, emphasizing the importance of likelihood methods in statistical inference.

Gaussian Process: Covariance and Correlation Functions

Explores Gaussian processes, covariance functions, intrinsic stationarity, and extreme applications in statistics.

Extreme Value Analysis: Applications and Consequences

Explores extremal limit theorems and statistical analysis for analyzing extreme events like Venezuela rainfall and Venice data.

Generalized Linear Models: Theory and Applications

Covers the theory and applications of Generalized Linear Models, including MLE, measures of fit, shrinkage, and special examples.

Extreme Value Theory

Explores the Extreme Value Theory and the impact of local dependence on extreme values.

Asymptotic Estimations

Explores completely multiplicative functions, inversion, Mobius functions, and asymptotic estimation in mathematics.

Gauss-Markov Theorem: Optimal Estimation

Explores the Gauss-Markov Theorem and the optimality of Least Squares Estimators in the Gaussian Linear model.

Learning Chemical Reaction Networks

Explores sparse learning of chemical reaction networks from trajectory data using data-based methods and learning approaches.