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Related lectures (32)

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Linear Regression: Basics and Estimation

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

Estimating Parameters: Confidence Intervals

Explores estimating parameters through confidence intervals in linear regression and statistics.

Sampling Distributions: Estimation

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

Point Estimation in Statistics

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

Bias and Variance in Estimation

Discusses bias and variance in statistical estimation, exploring the trade-off between accuracy and variability.

Financial Time Series: Stylized Facts and Modeling

Explores stylized facts and modeling of financial time series, including ARCH and GARCH processes.

Optimality in Decision Theory: Unbiased Estimation

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

Statistical Estimation

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

Dirichlet-Multinomial Model

Discusses the Dirichlet distribution, Bayesian inference, posterior mean and variance, conjugate priors, and predictive distribution in the Dirichlet-Multinomial model.

Estimating R: Maximum Likelihood Method

Explores estimating parameters in statistical models using the maximum likelihood method and the method of moments.