We propose an efficient, generalized, nonparametric, statistical KolmogorovSmirnov test for detecting distributional change in high-dimensional data. To implement the test, we introduce a novel, hierarchical, minimum-volume sets estimator to represent the distributions to be tested. Our work is motivated by the need to detect changes in data streams, and the test is especially efficient in this...

The present study assessed the vegetation response to climate in water-stressed northwest Bangladesh (NWB). quantile regression analysis was employed evaluate effect of and climatic extremes on vegetation. nonparametric correlation used assess influence for various time lags. Besides, modified Mann–Kendall (MMK) test conducted understand changes anticipate change impacts Satellite estimation no...

The selection of an adequate regression model is the basis for obtaining accurate and reproducible results during the bionalytical method validation. Given the wide concentration range, frequently present in bioanalytical assays, heteroscedasticity of the data may be expected. Several weighted linear and quadratic regression models were evaluated during the selection of the adequate curve fit u...

Drop plate technique has a priority and preference compared with the spread plate procedure, because of less time, quantity of media, effort requirement, little incubator space, and less labor intensive. The objective of this research was to compare the accuracy and fidelity of drop plate method vs. spread plate method by parametric and nonparametric statistical tests. For bacterial enumeration...

Simulations that generate very large data sets in one and many dimensions are increasingly common. Nonparametric density estimates based on these data sets are often required and estimators that can be generated and manipulated efficiently are needed. In computer graphics, for example, nonparametric density estimates over surfaces can be used to represent lighting functions [1]. These simulatio...

In the paper we presented the guidelines for the application of nonparametric statistical tests and post hoc procedures devised to perform multiple comparisons of machine learning algorithms. We emphasized it is necessary to distinguish between pairwise and multiple comparison tests. We showed that the pairwise Wilcoxon test, when employed to multiple comparisons, would lead to overoptimistic c...