This course is a natural followup for an undergraduate course in Mathematical Statistics. Students should be familiar with concepts like point estimation, confidence intervals and statistical testing, bias-variance tradeoff, and vector-valued distributions such as the multivariate Gaussian distribution. It is advised to have followed the Mastermath course Asymptotic Statistics, or at least be familiar with stochastic modes of convergence, the central limit theorem, and the delta-method. For some topics basic knowledge of matrix algebra is required.

Aim of the course
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 parametrized 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 the applications and methodology.

Hanne Kekkonen (please include the text NS2021 in the subject of the email).