Sampling Theory: Sufficiency and Ancillarity

Related lectures (30)

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

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

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

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

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

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

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

Explores statistical models, families of distributions, transformations, and their applications in probability theory.

Covers continuous random variables, probability density functions, and distributions, with practical examples.

Explores testing and estimating continuous distributions through interval partitioning and expected number calculations.