现代统计模型 Statistical Modeling and Methods - TA

The course deals with a variety of statistical models and methods that generalize classical linear regression to include many others that have been found useful in statistical analysis and applications. We will first review key concepts in linear regression analysis, and expand the scope in depth to generalized linear models, then head for several important directions: nonparametric regression and generalized additive models, linear and generalized linear mixed models, generalized estimating equations for correlated data, dimension reduction and so on (if time permits). The course is a mixture of theory and applications and includes computer projects featuring R programming.

  • InstructorFang Yao
  • Time: Thursday Session 3~4, Tuesday Session 5~6 (even week)
  • Place: The 3rd Teaching Building, Room 506
  • Prerequisites: multivariable calculus, linear/matrix algebra, probability theory, mathematical statistics, linear regression
  • References:
    • Rao, C.R.; Toutenburg, H.; Shalabh; Heumann, C.. Linear Models and Generalizations: Least Squares and Alternatives
    • McCullagh, P.; Nelder, J.A.. Generalized Linear Models
    • Diggle, P.; Heagerty, P.; Liang, K.Y.; Zeger, S. L.. Analysis of Longitudinal Data
    • Fan, J.; Gijbels, I.. Local Polynomial Modelling and Its Applications
    • Hastie, T.J.; Tibshirani, R.J.. Generalized Additive Models
    • James; Witten; Hastie; Tibshirani. An Introduction to Statistical Learning: with Applications in R
    • Faraway, J.. Linear Models with R
    • Faraway, J.. Extending the Linear Models with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models

学期: 

春季学期

开课学年: 

2021