Nonparametric, Semiparametric and Doubly-Nonparametric Generalized Linear Models

Abstract: 

Generalized linear models (GLMs) (McCullagh & Nelder, 1989) have become indispensable tools for analyzing agricultural, engineering and biomedical data. In this talk, we look at some (recent) extensions of GLMs, including classical nonparametric GLMs in which the mean-curve is nonparametric (e.g. Green & Silverman, 1994), semiparametric GLMs in which the error distribution is nonparametric (Huang, 2013), and doubly-nonparametric GLMs in which both the mean-curve and error distribution are nonparametric (current work). Some interesting data analysis examples and graphical tools will also be presented.

References

Green, P. J. and Silverman, B. W. (1994),Nonparametric Regression and Generalized Linear Models, Boca Raton: Chapman and Hall.

Huang, A. (2013), Joint estimation of the mean and error distribution in generalized linear models, J. Amer. Statist. Assoc.} (to appear)

McCullagh, P. and Nelder, J. A. (1989), Generalized Linear Models, London: Chapman \& Hall.