Sampling Distributions: Estimation

Related lectures (32)

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

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

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

Introduces Fisher Information for parameter estimation based on collected data.

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

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

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

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

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

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