Just as with other problems, there is a difference between randomization testing and bootstrap estimation. In the former, we are primarily interested in hypothesis testing, whereas the latter is ...

Just as with other problems, there is a difference between randomization testing and bootstrap estimation. In the former, we are primarily interested in hypothesis testing, whereas the latter is ...

Abstract: This paper applies randomization theory to the problem of selecting software test cases for software systems and applications in order to overcome the hurdle of high cost in testing ...

We consider the problem of conditional independence testing: given a response Y and covariates (X,Z), we test the null hypothesis that Y⫫X∣Z. The conditional ...

Covariate-adaptive designs are widely used to balance covariates and maintain randomization in clinical trials. Adaptive designs for discrete covariates and their asymptotic properties have been well ...

As a randomized variable in nonresponders and relapsed patients to assess its value in the choice of second-line therapy. At study entry, to ascertain whether the in vitro sensitivity to treatment ...

1.) Wagstaff, K., Cardie, C., Rogers, S., & Schrödl, S. (2001, June). Constrained k-means clustering with background knowledge. In ICML (Vol. 1, pp. 577-584). 2 ...

This project was created to understand how different randomization algorithms compare to eachother exclusively written in C++. Each group member created their own randomization algorithm then we ...