Covers conditional distributions and correlations in multivariate statistics, including partial variance and covariance, with applications to non-normal distributions.
Explores the consistency and asymptotic properties of the Maximum Likelihood Estimator, including challenges in proving its consistency and constructing MLE-like estimators.
Explores non-parametric estimation using kernel density estimators to estimate distribution functions and parameters, emphasizing bandwidth selection for optimal accuracy.
