Modern Regression: Maximum Likelihood Estimation

This lecture covers the maximum likelihood estimation (MLE) approach in modern regression, focusing on the log likelihood function and the profile log likelihood for estimating parameters B and o2. It discusses the MLEs of B and o2, the Newton-Raphson and EM algorithms for optimization, and inference on the coefficients. The lecture also introduces the concept of quasi-likelihood and its application in model comparison using deviance. Additionally, it explores the REML estimation method and its advantages over traditional MLE, emphasizing the importance of correct model specification and efficient estimation in large samples.