The aim of this course is to obtain a broad knowledge of nonparametric methods in statistics. Many methods in statistics are parametric in nature. In this case the distribution of the data is assumed to be parametrised by a finite-dimensional parameter. The basic idea of nonparametric methods is to drop, or relax, this often restrictive assumption. These methods thereby offer much more flexibility to model the data than classical parametric methods. The topics that we cover in this course form a mix of classical distribution free methods and more modern topics. The focus is on both application and theory of these methods. Examples will be illustrated using statistical computing tools, namely the statistical computing software R.



Basic knowledge of probability and statistics, and a sufficient level of mathematical maturity. It is highly recommended to have followed an undergraduate course in Mathematical Statistics. For topics 5-7 some undergraduate knowledge of matrix algebra is needed. Familiarity with R is useful, but not required.



Paulo Serra (TUE, MetaForum building, room MF4.088,

Phone: 040 247 2499