Semiparametric Inference

3 Credits · Level: Graduate

This course study essential aspects of parametric, semiparametric and nonparametric inferences. It includes optimality of Euclidean parameter estimation, basic properties of maximum likelihood estimate, convolution theorem, profile likelihood, Edgeworth expansion, Delta method, M-estimator, empirical process theory, weak convergence in functional space, convergence rate of general estimator, semiparametric/nonparametric efficiency, functional Delta method, density estimation, nonparametric regression (kernel method, basis method, spline method, reproducing kernel Hilbert space method), U-statistic, nonparametric maximum likelihood estimation, empirical likelihood, relative efficiency of nonparametric tests.