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 ...

As figure 5 shows, the parametric test dataset can now be used to forecast functional test yield, days or weeks ahead of the wafers reaching functional probe test, accelerating reaction time to ...

We propose a non-parametric change-point test for long-range dependent data, which is based on the Wilcoxon two-sample test. We derive the asymptotic distribution of the test statistic under the null ...

The gene classification algorithm is based on a series of NANOVA tests. The false discovery rate of each test is carefully controlled. Gene expression pattern in each group is modeled by a different ...

Stress testing in non-normal markets via entropy pooling The authors introduce a novel approach to stress testing portfolios of financial assets. The technique extends the parametric entropy pooling ...

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 ...