Statistical Estimators

Related lectures (30)

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Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

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

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.

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

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.

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

Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.

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