Applied Statistics

To obtain a broad knowledge of nonparametric methods in statistics. Many methods in statistics are parametric in nature. In this case the probability law of the data is assumed to be parametrized by a finite-dimensional parameter. The basic idea of nonparametric methods is to drop this assumption and to make as few assumptions as possible. These methods thereby offer much more flexibility to model the data than classical parametric methods. The topics that we will cover in this course form a mix of classical distribution free methods and more modern topics. We focus on application of the methods, though we will discuss some theoretical issues as well. Examples will be illustrated using the statistical computing package R.

In this course we will pay attention to the following topics:
- Empirical distribution, plug-in estimators, bootstrap (Ch. 10, 15)
- Goodness of fit tests (Ch. 6)
- Nonparametric (rank) tests and permutation tests (Ch. 6 -- 9, 15)
- Nonparametric estimation of distribution functions / densities / curves (Ch. 10 -- 13/14)
- Statistical learning (Ch. 17)
- Nonparametric Bayes (Ch 18. if time permits)
The chapters in brackets refer to chapters from the main text used in this course "Nonparametric Statistics with Applications to Science and Engineering" (Wiley Series in Probability and Statistics; authors: P.H. Kvam and B. Vidakovic).

Each class there will be three 45 minutes time slots.

Re-examination will be on August 21st, 2009 from 14.00 - 17.00h, in room BBL505