Statistical Estimation

Related lectures (32)

Page 3 of 4

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

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

Explains maximum likelihood estimation, MSE, Fisher information, and Cramér-Rao bound in statistical inference.

Covers the concept of point estimation in statistics, focusing on methods to estimate unknown parameters from a given sample.

Covers the fundamentals of detection and estimation theory, focusing on mean-squared error and hypothesis testing.

Introduces maximum likelihood estimation, discussing its properties and applications in statistical analysis.

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

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

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

Covers the fundamental concepts of statistics and their application in data science.