Estimator of Variance

Related lectures (32)

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Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

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

Explores sampling distributions, estimators' properties, and statistical measures for data science applications.

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

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

Explains statistical estimators for random variables and Gaussian distributions, focusing on error functions for integration.

Covers bias, variance, mean squared error, consistency, and maximum likelihood estimation in the Poisson model.

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

Explores statistical parameter estimation, sample accuracy, and Bernoulli variables' properties.

Explores statistical estimation, comparing estimators based on mean and variance, and delving into mean squared error and Cramér-Rao bound.