Sampling Theory: Sufficiency and Ancillarity

Related lectures (30)

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Explores entropy, minimally sufficient statistics, exponential families, and Gaussian sampling distributions.

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Covers the construction of confidence intervals for a normal distribution with unknown mean and variance.

Explores ancillary statistics, sufficiency, and minimally sufficient statistics in sampling distributions.

Covers regression with exponential family responses using Generalised Linear Models.

Covers statistical estimation methods, including maximum likelihood and Bayesian estimation.

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

Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.

Explores estimation measures of performance, including the Cramér-Rao bound and maximum likelihood estimation.

Covers various probability distributions and their applications in real-world scenarios.