Nonparametric Statistics (4V, 9.0 LP)
|SWS||Type||Course Form||CP (Effort)||Presence-Time / Self-Study|
|4||V||Lecture||9.0 CP||56 h||214 h|
|(4V)||9.0 CP||56 h||214 h|
Smoothing Methods for Estimating Functions:
- Smoothing methods for estimating functions (kernel estimator, local polynomial estimator, next-neighbour-estimator, smoothing splines) and their asymptotic analysis,
- Application in regression and image analysis,
- Data controlled choice of smoothing parameters with cross-validation,
- Spectral decomposition and spectral estimators for stationary time series.
Nonparametric Regression and Classification:
- Analysis of regression in higher dimensions on the basis of Boosting,
- General sieve estimators for functions and their asymptotic analysis,
- Trees of regression and classification, neuronal networks, expansion of orthogonal series and wavelets for example,
- Applications for estimating functions and solving classification problems with high dimensional causal variables.
- L. Györfy, M. Kohler, A. Krzyzak, H. Walk: A Distribution-Free Theory of Nonparametric Regressions,
- W. Härdle: Applied Nonparametric Regression,
- B. Silverman: Density Estimation for Statistics and Data Analysis.
Further literature will be announced in the lecture(s); exercise material is provided.
Requirements for attendance (informal)
- [MAT-10-1-M-2] Fundamentals of Mathematics (M, 28.0 LP)
- [MAT-60-12-M-4] Regression and Time Series Analysis (M, 9.0 LP)
Requirements for attendance (formal)
References to Course [MAT-62-12-K-7]
|[MAT-62-12-M-7]||Nonparametric Statistics||P: Obligatory||4V, 9.0 LP|