Mitchell Grant is a self-taught investor with over 5 years of experience as a financial trader. He is a financial content strategist and creative content editor. Timothy Li is a consultant, accountant ...

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 ...

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

Parametric tests make assumptions that aspects of the data follow some sort of theoretical probability distribution. Non-parametric tests or distribution free methods do not, and are used when the ...

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 ...

Seeds are planted on the interval [0, L] at various locations. Each seed has a location x and a potential germination time t ∈ [0, ∞), and it is assumed that the collection of such (x, t) pairs forms ...

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.

Please Note: Blog posts are not selected, edited or screened by Seeking Alpha editors. A lot of statistic analysis is based on parametric statistics. One of the most crucial assumptions is the bell ...

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, ...