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.
Practical information:
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- Prerequisites: Participants should have a good background in statistics, in particular in statistical inference.
- Price: €220-€1100
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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.
Course materials
The course material will be made available.
Background reading:
- Fan, J. and Gijbels, I. (1996). Local Polynomial Modeling and Its Applications. Chapman and Hall, New York.
- Hastie, T., Tibshirani, R. and Friedman, J. (2001). The Elements of Statistical Learning. Springer, New York.
- Simonoff, J.S. (1996). Smoothing Methods in Statistics. Springer, New York.