Frequentist inference

Related lectures (32)

Page 2 of 4

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

Explores independence and conditional probability in probability and statistics, with examples illustrating the concepts and practical applications.

Covers the construction of approximate confidence intervals using the central limit theorem for large sample sizes.

Explores moment-generating functions, linear combinations, and normality of random variables.

Covers the fundamentals of detection and estimation theory, focusing on mean-squared error and hypothesis testing.

Delves into the duality between confidence intervals and hypothesis tests, emphasizing the importance of precision and accuracy in estimation.

Covers the theory of sampling, focusing on statistics for mathematicians.

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

Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.

Explores regular exponential family models, unifying distributions like Poisson, binomial, and normal under a common framework.