Bias, Variance, Consistency, EMV

Related lectures (32)

Page 1 of 4

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

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

Introduces Fisher Information for parameter estimation based on collected data.

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

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

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

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

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