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Lecture
Optimal Testing Methods
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Related lectures (32)
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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.
Classification Detection
Covers binary hypothesis testing and decision functions in specific scenarios.
Hypothesis Testing: Wilks' Theorem
Explores hypothesis testing using Wilks' Theorem, likelihood ratio statistics, p-values, interval estimation, and confidence regions.
Detection & Estimation
Covers binary classification, hypothesis testing, likelihood ratio tests, and decision rules.
Hypothesis Testing: Neyman-Pearson Framework
On hypothesis testing explores the Neyman-Pearson framework, test functions, errors, and likelihood ratio tests.
Likelihood Ratio Test: Neyman-Pearson Lemma
Explores likelihood ratio tests and the Neyman-Pearson Lemma for statistical hypothesis testing.
Statistical Hypothesis Testing
Covers statistical hypothesis testing, likelihood estimation, and confidence intervals construction.
Statistical Theory: Cramér-Rao Bound & Hypothesis Testing
Explores the Cramér-Rao bound, hypothesis testing, and optimality in statistical theory.
Hypothesis Testing in Statistics
Explores hypothesis testing in statistics, focusing on decision-making based on sample data and controlling error probabilities.
Likelihood Ratio Test: Detection & Estimation
Covers the likelihood ratio test for detection and estimation in statistical analysis.
