Estimation Methods: Bias-Variance Tradeoff

Related lectures (32)

Page 3 of 4

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

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

Introduces linear regression concepts, from X-bands creation to slope estimator properties and tests.

Covers the construction of confidence intervals for a normal distribution with unknown mean and variance.

Covers confidence intervals, R2, and examples on cement heat evolution and car horsepower-MPG relationships.

Covers confidence intervals for Gaussian means, Student distribution, and Wald confidence intervals for maximum likelihood estimators.

Explores linear estimation, optimal criteria, and the orthogonality principle for good choices in estimation.

Covers error estimation in Latin Hypercube Sampling, emphasizing the importance of accurate variance estimation.

Explores hypothesis testing using Wilks' Theorem, likelihood ratio statistics, p-values, interval estimation, and confidence regions.

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