TENTATIVE OUTLINE

Overview of smoothing:

Standard least squares (normal likelihood) methods:

Using other likelihoods with the above standard methods (this can cover quite a range - like all of parametric statistics!)

Additive models, semi-parametric models

Functional data analysis - brief overview

After this, topics will depend on people's interest. Possible topics:

WORK FOR COURSE

There will be no exams.

TEXT/REFERENCES

There is no text for the course. I have some latexed class notes (currently under revision). We'll also use articles and parts of different books.

Here are some books on smoothing, with my comments. They are on 3 day reserve at UBC.

Bowman and Azzalini (1997) Applied smoothing techniques for data analysis : the kernel approach with S-Plus illustrations (missing, have requested replacement)

Eubank, R. (1988). Spline Smoothing and Nonparametric Regressions.

Fan, Jianqing and Gijbels (1996). Local polynomial modelling and its applications.

Green, P.J. and B.W. Silverman (1994). Nonparametric Regression and Generalized Linear Models. This book covers penalized likelihood methods.

Hardle, W. (1990). Applied Nonparametric Regression.

Hastie, T. and R. Tibshirani (1990). Generalized Additive Models. Chapters 1 - 3 give a nice summary of smoothing methods and ideas.

Ramsay and Silverman (1997). Functional Data Analysis. Not on reserve yet.

Ramsay and Silverman (2002). Applied Functional Data Analysis: Methods and Case Studies. On order.

Simonoff, J. (1996). Smoothing methods in statistics. Nice overview, not very theoretical, pretty practical.