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Note: Students who have already introduced 7010316 Multivariate Statistical Analysis II/Non- and Semiparametric Modeling into their studies may not take Non- and Semiparametric Modeling again.
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 for this purpose the aim of the course is to establish self written python code from existing R and Matlab quantlets (www.quantlet.de).
The registration in the respective Moodle course is obligatory.
- 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
- Semi- and Nonparametric Estimation of Treatment Effects Doubly-Robust Methods
- Generalized Random Forest
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| Literatur |
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 |
