Non- and Semiparametric Modelling

Instructor: 
Description: 

The course Non- and Semiparametric Modelling gives an overview over the flexible regression methods. The course starts with an introduction into the density estimation (histogram, kernel density estimation). Nonparametric regression methods and their applications are discussed. Furthermore additive models will be introduced in the course. At the end of the course the students will be able to implement methods to solve practical problems.

Literature:
Härdle, Müller, Sperlich, Werwatz (2004): Non- and Semiparametric Modelling, Springer
Fan, J. and Gijbels, I. (1996): Local Polynomial Modelling and Its Applications, Chapman and Hall, New York
Härdle, W. (1990): Applied Nonparametric Regression, Econometric Society Monographs No. 19, Cambridge University Press
Härdle, W. (1991): Smoothing Techniques, With Implementations in S, Springer, New York
Härdle, Klinke, Müller (1999): XploRe - Academic Edition, The Interactive Statistical Computing Environment, Springer, New York
Scott, D. W. (1992): Multivariate Density Estimation: Theory, Practice, and Visualization, John Wiley & Sons, New York, Chichester
Silverman, B. W. (1986): Density Estimation for Statistics and Data Analysis, Vol. 26 of Monographs on Statistics and Applied Probability, Chapman and Hall, London
Wand, M. P. and Jones, M. C. (1995): Kernel Smoothing, Chapman and Hall, London
Yatchew, A., (2003): Semiparametric Regression for Applied Econometrician, Cambridge University Press, Cambridge

Credits: 
6.00
Affiliation: 
Humboldt-Universität zu Berlin