Course No.：21609Z
Period：14
Credits：0.5
Course Category：Lecture
Primary Coverage：
Talks 1 -2 Techniques for Nonparametric Models
Nonparametric regression models have been used to explore the complicated relation between the response and the predictors of interest because it may be very difficult even impossible to give any close¬ form to express this relationship. I will present an up-¬to-¬date picture on the state of nonparametric regression. Several fundamental techniques such as local polynomial, smoothing spline, and penalized spline will be presented.
Talks 3-4 Semi-parametric Generalized Partially Linear Models
More recently a lot of efforts have been done to make a trade¬off between the para¬metric simplicity and nonparametric flexibility, and semi-parametric regression models have been widely studied. Emphasis is on generalized partially linear models (GPLM), in which the conditional expectation of the response variable given covariates depends on some variable in a linear way but nonlinearly related to other variable. They compro¬mise the interpretation of traditional (generalized) linear models and flexibility of non¬parametric regression. GPLM contain generalizations of multiple linear regression mod¬els, generalized linear models, and partially linear models. This lecture will cover the methodological aspects of profile likelihood and backfitting in GPLM for cross¬-sectional and longitudinal data.
Talk 5 Measurement Errors Models
Covariates measured with errors occur frequently in biological epidemiologic research. Two well¬ developed approaches in this field: regression calibration and simulation extrap¬olation, will be introduced.
References:
Carroll, R. J., Ruppert, D., Stefanski, L. A., and Crainiceanu, C. M. (2006). Measurement Error in Nonlinear Models, 2nd ed. Chapman and Hall, New York.
Fan, J. and Gijbels, I. (1996). Local Polynomial Modelling and Its Applications. Vol. 66 of Monographs on Statistics and Applied Probability, Chapman and Hall, New York.
Haerdle, W., Liang, H. and Gao, J. (2000). Partially Linear Models. Springer Physica¬Verlag, Heidelberg.
Hastie, T. J. and Tibshirani, R. J. (1990). Generalized Additive Models, Vol. 43 of Mono¬graphs on Statistics and Applied Probability, Chapman and Hall, London.
Wand, M. and Jone, (1995). Kernel Smoothing, Vol. 60 of Monographs on Statistics and Applied Probability, Chapman and Hall, London.
Author：Hua Liang
