Covers Kernel Density Estimation focusing on bandwidth selection, curse of dimensionality, bias-variance tradeoff, and parametric vs nonparametric models.
Covers spectral estimation techniques like tapering and parametric estimation, emphasizing the importance of AR models and Whittle likelihood in time series analysis.
Explores the Stein Phenomenon, showcasing the benefits of bias in high-dimensional statistics and the superiority of the James-Stein Estimator over the Maximum Likelihood Estimator.
