Statistical Estimation

Related lectures (32)

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Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

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

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

Introduces Fisher Information for parameter estimation based on collected data.

Covers estimation of parameters, MSE, Fisher information, and the Rao-Blackwell Theorem.

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

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

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

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

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.