Covers conditional distributions and correlations in multivariate statistics, including partial variance and covariance, with applications to non-normal distributions.
Explores non-parametric estimation using kernel density estimators to estimate distribution functions and parameters, emphasizing bandwidth selection for optimal accuracy.
Covers Kernel Density Estimation focusing on bandwidth selection, curse of dimensionality, bias-variance tradeoff, and parametric vs nonparametric models.
