This 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 advantages ...

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 ...

We provide novel, high-order accurate methods for non-parametric inference on quantile differences between two populations in both unconditional and conditional settings. These quantile differences ...

We use influence functions as a basic tool to study unconditional nonparametric and parametric expected shortfall (ES) estimators with regard to returns data influence, standard errors and coherence.

Understanding some statistics is important for general science literacy. Below are some common statistics resources that may be useful for your project work. To support your professional development, ...

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 ...

We present a non-parametric method for calibrating jump–diffusion and, more generally, exponential Lévy models to a finite set of observed option prices. We show that the usual formulations of the ...