Non- and Semiparametric Modelling

Instructor: 
Time I: 
Thursday,
10:00am to 12:00pm
Venue I: 
HU Berlin, Spandauer Str. 1, Room 21a
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

Students can purchase the Professional Edition of XploRe and/or a bookset for a reduced price. For details please ask the lecturer or send an email to mdtech@mdtech.de.

Exam:
written
Credits: 
6.00
Program: 
Semester: 
Fall 2012
Affiliation: 
Humboldt-Universität zu Berlin