The Kolmogorov-Smirnov (KS) test is popularly used in many applications, such as anomaly detection, astronomy, database security and AI systems. One challenge remained untouched how we can obtain an explanation on why a set fails the KS test. In this paper, tackle problem of producing counterfactual explanations for data failing Concept-wise, propose notion most comprehensible explanations, whi...

In this paper, we are interested in extending the classical KolmogorovSmirnov homogeneity test to compare two samples of interval-valued observed measurements. In such a case, the test result is interval-valued, and one major difficulty is to find the bounds of this set. We propose a very efficient computational method for approximating these bounds by using a p-box (pairs of upper and lower cu...

When a reliability analysis, survival analysis, or discrete-event simulation input modeling analysis is performed, it is typically the case that a modeler collects data associated with some system under consideration. Although this raw data provides an initial insight into the stochastic elements of the system, often it is desirable to construct a model of the system which is tractable for math...

We prove the following conjecture of Narayana: there are no nontrivial dominance refinements of the Smirnov two-sample test if and only if the two sample sizes are relatively prime. We also count the number of natural significance levels of the Smirnov two-sample test in terms of the sample sizes and relate this to the Narayana conjecture. In particular, Smirnov tests with relatively prime samp...

We prove the following conjecture of Narayana: there are no nontrivial dominance reene-ments of the Smirnov two-sample test if and only if the two sample sizes are relatively prime. We also count the number of natural signiicance levels of the Smirnov two-sample test in terms of the sample sizes and relate this to the Narayana conjecture. In particular, Smirnov tests with relatively prime sampl...