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The two-sample Kolmogorov-Smirnov test is a widely used statistical test for detecting whether two samples are likely to come from the same distribution. Implementations typically recur on an article of Hodges from 1957. The advances in…

Computation · Statistics 2021-09-27 Thomas Viehmann

The cumulative distribution and quantile functions for the one-sided one sample Kolmogorov-Smirnov probability distributions are used for goodness-of-fit testing. While the Smirnov-Birnbaum-Tingey formula for the CDF appears straight…

Computation · Statistics 2018-02-21 Paul van Mulbregt

Continuous goodness-of-fit testing is a classical problem in statistics. Despite having low power for detecting deviations at the tail of a distribution, the most popular test is based on the Kolmogorov-Smirnov statistic. While similar…

Methodology · Statistics 2019-09-16 Amit Moscovich , Boaz Nadler , Clifford Spiegelman

We present a novel method for computing reachability probabilities of parametric discrete-time Markov chains whose transition probabilities are fractions of polynomials over a set of parameters. Our algorithm is based on two key…

Software Engineering · Computer Science 2014-03-28 Nils Jansen , Florian Corzilius , Matthias Volk , Ralf Wimmer , Erika Ábrahám , Joost-Pieter Katoen , Bernd Becker

When comparing two distributions, it is often helpful to learn at which quantiles or values there is a statistically significant difference. This provides more information than the binary "reject" or "do not reject" decision of a global…

Statistics Theory · Mathematics 2018-08-16 Matt Goldman , David M. Kaplan

Permutation tests are among the simplest and most widely used statistical tools. Their p-values can be computed by a straightforward sampling of permutations. However, this way of computing p-values is often so slow that it is replaced by…

Computation · Statistics 2018-07-27 Jean-Marie Droz

Permutation tests are amongst the most commonly used statistical tools in modern genomic research, a process by which p-values are attached to a test statistic by randomly permuting the sample or gene labels. Yet permutation p-values…

Applications · Statistics 2016-03-21 Belinda Phipson , Gordon K. Smyth

The cumulative distribution and quantile functions for the two-sided one sample Kolmogorov-Smirnov probability distributions are used for goodness-of-fit testing. The CDF is notoriously difficult to explicitly describe and to compute, and…

Computation · Statistics 2018-03-02 Paul van Mulbregt

The focus of this paper is the efficient computation of counterparty credit risk exposure on portfolio level. Here, the large number of risk factors rules out traditional PDE-based techniques and allows only a relatively small number of…

Computational Finance · Quantitative Finance 2018-02-05 Cornelis S. L. de Graaf , Drona Kandhai , Christoph Reisinger

$\textbf{Motivation:}$ Small $p$-values are often required to be accurately estimated in large-scale genomic studies for the adjustment of multiple hypothesis tests and the ranking of genomic features based on their statistical…

Applications · Statistics 2023-08-29 Yang Shi , Mengqiao Wang , Weiping Shi , Ji-Hyun Lee , Huining Kang , Hui Jiang

Two-sided statistical tests and p-values are well defined only when the test statistic in question has a symmetric distribution. A new two-sided p-value called conditional p-value $P_C$ is introduced here. It is closely related to the…

Statistics Theory · Mathematics 2008-10-14 Elena Kulinskaya

Likelihood ratio tests are a widely used method in global analyses in particle physics. The computation of the statistical significance (p-value) of these tests is usually done with a simple formula that relies on Wilks' theorem. There are,…

High Energy Physics - Phenomenology · Physics 2013-08-21 Martin Wiebusch

Software packages usually report the results of statistical tests using p-values. Users often interpret these by comparing them to standard thresholds, e.g. 0.1%, 1% and 5%, which is sometimes reinforced by a star rating (***, **, *). We…

Methodology · Statistics 2019-11-05 Axel Gandy , Georg Hahn , Dong Ding

The author's recent research papers, "Cumulative deviation of a subpopulation from the full population" and "A graphical method of cumulative differences between two subpopulations" (both published in volume 8 of Springer's open-access…

Methodology · Statistics 2024-04-09 Mark Tygert

The PC algorithm allows investigators to estimate a complete partially directed acyclic graph (CPDAG) from a finite dataset, but few groups have investigated strategies for estimating and controlling the false discovery rate (FDR) of the…

Machine Learning · Statistics 2017-05-11 Eric V. Strobl , Peter L. Spirtes , Shyam Visweswaran

Permutation tests are widely used for statistical hypothesis testing when the sampling distribution of the test statistic under the null hypothesis is analytically intractable or unreliable due to finite sample sizes. One critical challenge…

Computation · Statistics 2023-08-29 Yang Shi , Huining Kang , Ji-Hyun Lee , Hui Jiang

In genetic association studies, detecting disease-genotype associations is a primary goal. For most diseases, the underlying genetic model is unknown, and we study seven robust test statistics for monotone association. For a given test…

Methodology · Statistics 2020-04-13 Mette Langaas , Øyvind Bakke

The hierarchy of exact equations is given that relates two-spatial-point velocity structure functions of arbitrary order with other statistics. Because no assumption is used, the exact statistical equations can apply to any flow for which…

Fluid Dynamics · Physics 2019-06-19 Reginald J. Hill

We present an efficient algorithm that, given a discrete random variable $X$ and a number $m$, computes a random variable whose support is of size at most $m$ and whose Kolmogorov distance from $X$ is minimal, also for the one-sided…

Machine Learning · Statistics 2022-07-19 Liat Cohen , Tal Grinshpoun , Gera Weiss

This paper investigates the estimation of the self-similarity parameter in fractional processes. We re-examine the Kolmogorov-Smirnov (KS) test as a distribution-based method for assessing self-similarity, emphasizing its robustness and…

Methodology · Statistics 2025-02-12 Daniele Angelini , Sergio Bianchi
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