Maximum Likelihood Estimation

This lecture by the instructor covers the topic of Maximum Likelihood Estimation (MLE) in statistics. It delves into the concept of estimation, specifically focusing on ML Estimation-Distribution, Shrinkage Estimation, and Loss functions. The lecture explores the theoretical foundations of MLE, including the Central Limit Theorem and the Stein theorem. It also discusses the application of MLE in real-world scenarios, such as in the context of exponential distribution and decision theory. The presentation emphasizes the importance of understanding risk, loss functions, and hypothesis testing in statistical inference.