Lecture
This lecture covers the concepts of spectral estimation in time series analysis, including topics such as imaging kernels, tapering methods, parametric estimation, and multitapering. The instructor explains how to estimate the spectrum of a process, reduce bias in spectral estimators, and analyze the performance of periodograms. The lecture also delves into AR models, Yule-Walker estimation, and the importance of AR processes in approximating continuous spectra. Various algorithms and methods for spectral estimation are discussed, providing insights into the analysis of time series data.