Course TopicsThis short course will provide an overview of non-parametric statistical techniques. The course will first describe what non-parametric statistics are, when they should be used, and their ...

Nonparametric methods form an important core of statistical techniques and are typically used when data do not meet parametric assumptions. Understanding the foundation of these methods, as well as ...

Nonparametric estimation and U-statistics have emerged as vital tools in modern statistical analysis, offering robust alternatives to traditional parametric methods.

Asymptotic lower bounds for estimation of the parameters of models with both parametric and nonparametric components are given in the form of representation theorems (for regular estimates) and ...

Moreover, any prior parametric or nonparametric, may be approximated arbitrarily closely by a prior which is a mixture of Dirichlet processes. These results have implications in Bayesian inference.

An introduction to analysing quantitative data including topics such as, understanding the distribution of data variables, and parametric and non-parametric statistical tests.

The Kruskal Wallis test and other non-parametric (or distribution-free) tests are useful to test hypotheses when the assumption for normality of the data does not hold. They make no assumptions about ...

The Mann-Whitney U Test, also known as the Wilcoxon Rank Sum Test, is a non-parametric statistical test used to compare two samples or groups. The Mann-Whitney U Test assesses whether two sampled ...