The two-sample Kolmogorov-Smirnov (KS) test is often used to decide whether two random samples have the same statistical distribution. A popular modification of the KS test is to use a signed version of the KS statistic to infer whether the values of one sample are statistically larger than the values of the other. The underlying hypotheses of the KS test are intrinsically incompatible with thi...

Generic side-channel distinguishers aim at revealing the correct key embedded in cryptographic modules even when few assumptions can be made about their physical leakages. In this context, KolmogorovSmirnov Analysis (KSA) and Partial Kolmogorov-Smirnov analysis (PKS) were proposed respectively. Although both KSA and PKS are based on Kolmogorov-Smirnov (KS) test, they really differ a lot from ea...

The use of the Kolmogorov-Smirnov (KS) statistic for testing hypotheses regarding activation in blood oxygenation level-dependent functional MRI data is critiqued on both theoretical and empirical grounds. Theoretically, it is argued that the KS test is formally unable to support inferences of interest to most neuro-imaging studies and has reduced sensitivity compared with parametric alternativ...

This paper describes a simple, non-parametric and generic test of the equivalence of Receiver Operating Characteristic (ROC) curves based on a modified Kolmogorov-Smirnov (KS) test. The test is described in relation to the commonly used techniques such as the Area Under the ROC curve (AUC) and the Neyman-Pearson method. We first review how the KS test is used to test the null hypotheses that th...

A filter algorithm using F-measure has been used with feature redundancy removal based on the Kolmogorov-Smirnov (KS) test for rough equality of statistical distributions. As a result computationally efficient K-S CorrelationBased Selection algorithm has been developed and tested on three high-dimensional microarray datasets using four types of classifiers. Results are quite encouraging and sev...

An important problem in statistics is to obtain information about the form of the population from which the sample is drawn. Goodness of fit test is employed to determine how well the observed sample data "fits" some proposed model. The standard goodness of fit statistics are inappropriate when the parameters of the hypothesized distribution are estimated from the data used for the test. In thi...

BACKGROUND Multiple imputation (MI) is becoming increasingly popular as a strategy for handling missing data, but there is a scarcity of tools for checking the adequacy of imputation models. The Kolmogorov-Smirnov (KS) test has been identified as a potential diagnostic method for assessing whether the distribution of imputed data deviates substantially from that of the observed data. The aim of...

Discrimination between open water and sea ice in SAR imagery can still pose a problem to the ice analysts during manual interpretation. To help them in this task, new algorithm have been tested which is based on the user first manually identifying a particular surface type in a SAR image (e.g., open water area or sea ice of particular concentration or ice type) then the program will automatical...

Many works have been carried out to compare the efficiency of several goodness of fit procedures for identifying whether or not a particular distribution could adequately explain a data set. In this paper a study is conducted to investigate the power of several goodness of fit tests such as Kolmogorov Smirnov (KS), Anderson-Darling(AD), CramervonMises (CV) and a proposed modification of Kolmogo...

This paper derives asymptotic approximations to the power of Cramer-von Mises (CvM) style tests for inference on a finite dimensional parameter defined by conditional moment inequalities in the case where the parameter is set identified. Combined with power results for Kolmogorov-Smirnov (KS) tests, these results can be used to choose the optimal test statistic, weighting function and, for test...