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Humboldt-Universität zu Berlin - Statistik

Non- and Semiparametric Modelling (VL)

Kategorie
Master
Lehrende(r)
M. Müller

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.

Course Outline

  • Introduction
    Parametric Regression
    Nonparametric Regression
    Semiparametric Regression
  • Nonparametric Density Estimation
    Histogram, Average Shifted Histogram
    Kernel Density Estimation (KDE) , Motivation and Derivation
    KDE - Statistical Properties
    KDE - Smoothing Parameter Selection
    KDE - Choosing the Kernel
    Confidence Intervals and Confidence Bands
    Multivariate Kernel Density Estimation
  • Nonparametric Regression
    Univariate Kernel Regression
    Other Smoothers (Regression Splines, Orthogonal Series)
    Smoothing Parameter Selection
    Confidence Regions and Tests
    Multivariate Kernel Regression
    Applications

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.
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