Statistical Models: Sampling and Hypothesis Testing
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Explores sufficient statistics, data compression, and their role in statistical inference, with examples like Bernoulli Trials and exponential families.
Explores statistical inference, sufficiency, and completeness, emphasizing the importance of sufficient statistics and the role of complete statistics in data reduction.
Explores the consistency and asymptotic properties of the Maximum Likelihood Estimator, including challenges in proving its consistency and constructing MLE-like estimators.
