Dynamischer Default-Fachbereich geändert auf MAT

Course MAT-62-12-K-7

Nonparametric Statistics (4V, 9.0 LP)

Course Type

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

Basedata

SWS	4V
CP, Effort	9.0 CP = 270 h
Position of the semester	1 Sem. irreg.
Level	[7] Master (Advanced)
Language	[EN] English
Lecturers	Redenbach, Claudia, Prof. Dr. (PROF \| DEPT: MAT) Stockis, Jean-Pierre, Dr. (WMA \| DEPT: MAT) + further Lecturers of the department Mathematics
Area of study	[MAT-STO] Stochastics/Statistics/Financial Mathematics
Additional informations
Livecycle-State	[NORM] Active

Contents

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.

Literature

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.

Materials

Further literature will be announced in the lecture(s); exercise material is provided.

Requirements for attendance (informal)

Modules:

Requirements for attendance (formal)

None

References to Course [MAT-62-12-K-7]

Module	Name	Context
[MAT-62-12-M-7]	Nonparametric Statistics	P: Obligatory	4V, 9.0 LP