Estimation: Measures of Performance

Related lectures (31)

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Explores the Cramér-Rao bound, hypothesis testing, and optimality in statistical theory.

Covers maximum likelihood estimation, including the Rao-Blackwell Theorem proof and practical examples of deriving estimators.

Explains Fisher information, Cramér-Rao inequality, and MLE properties, including invariance and asymptotics.

Introduces Fisher Information for parameter estimation based on collected data.

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

Covers the concepts of parameter estimation, including unbiased estimators and Fisher information.

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

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

Covers Maximum Likelihood Estimation in statistical inference, discussing MLE properties, examples, and uniqueness in exponential families.

Covers parameter estimation, Fisher information, unbiased estimator, and exponential distributions.