Likelihood Ratio Test: Detection & Estimation

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Covers the Bayes discriminant rule for allocating individuals to populations based on measurements and prior probabilities.

Explores statistical tests like the Wald test and p-values, emphasizing their calculation and interpretation.

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Covers classification concepts, medical screening tests, and decision rules.

Explores logistic regression model interpretation, parameter estimation, and model comparison using likelihood ratio tests.

Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.

Explores hypothesis testing, Wilks' theorem, p-values, confidence intervals, and pivotal quantities.

Covers jacamar data analysis, smoking data models, and challenges with log-linear models in visual impairment data.

Explores the Method of Moments, Bias-Variance tradeoff, Consistency, Plug-In Principle, and Likelihood Principle in point estimation.

Explores the impact of a binary covariate on binary responses using 2x2 tables.

Explores optimality in decision theory and unbiased estimation, emphasizing sufficiency, completeness, and lower bounds for risk.

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