Estimation and Confidence Intervals

This lecture covers the estimation of parameters, standard errors, and confidence intervals using the central limit theorem. It explains how to find confidence intervals for unknown parameters, such as the standard deviation, in both regular and non-regular cases. The instructor demonstrates the use of estimators, maximum likelihood estimation, and the delta method to derive approximate confidence intervals. Additionally, the lecture discusses the importance of regularity conditions in statistical inference and provides examples to illustrate the concepts. Practical applications, such as simulating values and using software like R for statistical computations, are also explored.