Estimation: Mean-Squared Error and Fisher Information

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Covers the concept of mean-square-error inference and optimal estimators for inference problems using different design criteria.

Explores statistical models, parameter estimation, and sampling distributions in probability and statistics.

Explores estimators, bias, and efficiency in statistics, emphasizing the trade-off between bias and variability.

Explores sampling distributions, estimation methods, and consistency in parameter estimation.

Discusses bias and variance in statistical estimation, exploring the trade-off between accuracy and variability.

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Explores point estimation in statistics, discussing bias, variance, mean squared error, and consistency of estimators.

Explores linear prediction, prediction coefficients, mean squared error minimization, and the Levinson-Durbin algorithm in signal processing.

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

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

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

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

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