Advanced non-parametric statistics and smoothing
Nonparametric smoothing techniques are an important class of tools for identifying the true signal hidden in noisy data. These tools are widely used in statistical analysis in a variety of application areas. This course will provide the students with a thorough overview of the most important smoothing techniques (such as kernel smoothing, local polynomial fitting, spline smoothing, wavelet decomposition, regularization techniques, …). The course will address theoretical and computational aspects. We will discuss how to use these techniques in different settings (e.g. in a univariate or multivariate regression setting, in case of incomplete data, …). The course includes illustrations with data examples and the use of the R software.
Praktische info:
Leertraject
Inschrijven?
- Voorwaarden: Participants should have a good background in statistics, in particular in statistical inference.
- Prijs: (PhD) students KU Leuven and Association KU Leuven: € 220 - Staff KU Leuven and Association KU Leuven, other (PhD) students: € 350 - Non profit/social sector: € 580 - Private sector: € 1100
georganiseerd door:
Lesgever/spreker
Irène Gijbels is a mathematical statistician at KU Leuven, and an expert on nonparametric statistics. She has also collaborated with TopSportLab, a KU Leuven spin-off, on software for risk assessment of sports injuries.
