Generalized Linear Models extends classical regression models tonon-normal response variables and allows a non-linear relationbetween the mean of the responses and the predictors. In addition,when the responses are correlated or show overdispersion, one canadd a linear combination of random...

This dissertation proposes a nonparametric quasi-likelihood approach to estimate regression coefficients in the class of generalized linear regression models for longitudinal data analysis, where the covariance matrices of the longitudinal data are totally unknown but are smooth functions of...

The additive model overcomes the "curse of dimensionality" in general nonparametric regression problems, in the sense that it achieves the optimal rate of convergence for a one-dimensional smoother. Meanwhile, compared to the classical linear regression model, it is more flexible in defining an...