- 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.