This course provides a rigorous introduction to classical models and tools in applied statistics. About 50% of the lectures use exponential family structure to motivate generalized linear models and other useful applied techniques, and the other half cover models and methods in semiparametric (~20%) and nonparametric statistics (~30%). The main topics include exponential family, generalized linear model, bootstrap, survival analysis, missing data analysis, empirical Bayes, model selection, semiparametric models, average treatment effect, double robust estimation, density estimation, nonparametric regression, local polynomials and splines, wavelets methods, isotonic regression.
Time and location
Lecture: Tue 2:00 - 3:40 PM at Silver Center 206 (31 Washington Pl)
Lab: Fri 11:15 AM - 12.05 PM at Silver Center 401 (31 Washington Pl)
Reading Materials
Handwritten lecture notes will be provided before each lecture.