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Testing for differences between two groups is among the most frequently carried out statistical methods in empirical research. The traditional frequentist approach is to make use of null hypothesis significance tests which use p values to reject a null hypothesis. Recently, a lot of research has emerged which proposes Bayesian versions of the most common parametric and nonparametric frequentist two-sample tests. These proposals include Student’s two-sample t-test and its nonparametric counterpart, the Mann–Whitney U test. In this paper, the underlying assumptions, models and their implications for practical research of recently proposed Bayesian two-sample tests are explored and contrasted with the frequentist solutions. An extensive simulation study is provided, the results of which demonstrate that the proposed Bayesian tests achieve better type I error control at slightly increased type II error rates. These results are important, because balancing the type I and II errors is a crucial goal in a variety of research, and shifting towards the Bayesian two-sample tests while simultaneously increasing the sample size yields smaller type I error rates. What is more, the results highlight that the differences in type II error rates between frequentist and Bayesian two-sample tests depend on the magnitude of the underlying effect.

We introduce fully non-parametric two-sample tests for testing the null hypothesis that the samples come from the same distribution if the values are only indirectly given via current status censoring. The tests are based on the likelihood ratio principle and allow the observation distributions to be different for the two samples, in contrast with earlier proposals for this situation. A bootstrap method is given for determining critical values and asymptotic theory is developed. A simulation study, using Weibull distributions, is presented to compare the power behaviour of the tests with the power of other non-parametric tests in this situation.

We propose a robust methodology to evaluate the performance and computational efficiency of non-parametric two-sample tests, specifically designed for high-dimensional generative models in scientific applications such as in particle physics. The study focuses on tests built from univariate integral probability measures: the sliced Wasserstein distance and the mean of the Kolmogorov–Smirnov (KS) statistics, already discussed in the literature, and the novel sliced KS statistic. These metrics can be evaluated in parallel, allowing for fast and reliable estimates of their distribution under the null hypothesis. We also compare these metrics with the recently proposed unbiased Fréchet Gaussian distance and the unbiased quadratic Maximum Mean Discrepancy, computed with a quartic polynomial kernel. We evaluate the proposed tests on various distributions, focusing on their sensitivity to deformations parameterized by a single parameter ε . Our experiments include correlated Gaussians and mixtures of Gaussians in 5, 20, and 100 dimensions, and a particle physics dataset of gluon jets from the JetNet dataset, considering both jet- and particle-level features. Our results demonstrate that one-dimensional-based tests provide a level of sensitivity comparable to other multivariate metrics, but with significantly lower computational cost, making them ideal for evaluating generative models in high-dimensional settings. This methodology offers an efficient, standardized tool for model comparison and can serve as a benchmark for more advanced tests, including machine-learning-based approaches.

We propose a non-parametric change-point test for long-range dependent data, which is based on the Wilcoxon two-sample test. We derive the asymptotic distribution of the test statistic under the null hypothesis that no change occurred. In a simulation study, we compare the power of our test with the power of a test which is based on differences of means. The results of the simulation study show that in the case of Gaussian data, our test has only slightly smaller power than the 'difference-of-means' test. For heavy-tailed data, our test outperforms the 'difference-of-means' test.

We consider variable screening in high-dimensional binary classification. First, we propose nonparametric test statistics for the problem of the two-sample distribution comparison. These test statistics combine the merits of the chi-squared and Kolmogorov-Smirnov statistics, and provide new insights into the equality test of the unspecified distributions underlying the two independent samples. Based on our new statistics, we propose a marginal screening procedure and a pairwise joint screening procedure for detecting important variables in high-dimensional binary classification. Both screening procedures have the consistent screening property, which is stronger than the sure screening property of most existing methods. The marginal screening procedure is much more powerful than other methods over a broad range of cases, and the pairwise joint screening procedure provides a way of detecting variables with a joint effect, but no marginal effect. Extensive simulations and a real-data application show the effectiveness and advantages of the proposed methods.

K-sample testing problems arise in many scientific applications and have attracted statisticians' attention for many years. We propose an omnibus nonparametric method based on an optimal discretization (aka \"slicing\") of continuous random variables in the test. The novelty of our approach lies in the inclusion of a term penalizing the number of slices (i.e., the resolution of the discretization) so as to regularize the corresponding likelihood-ratio test statistic. An efficient dynamic programming algorithm is developed to determine the optimal slicing scheme. Asymptotic and finite-sample properties such as power and null distribution of the resulting test statistic are studied. We compare the proposed testing method with some existing well-known methods and demonstrate its statistical power through extensive simulation studies as well as a real data example. A dynamic slicing method for the one-sample testing problem is further developed and studied under the same framework. Supplementary materials including technical derivations and proofs are available online.

In this paper we address a two-sample problem in the context of point processes on linear networks. The aim is to determine whether two given point patterns defined over the same linear network and under the assumption of Poissonness, share the same spatial structure. To do so, a Kolmogorov–Smirnov and a Cramér von Mises type test statistics are developed and analysed through an extensive simulation study. We have included different types of networks, balanced and unbalanced sample sizes, and homogeneous and inhomogeneous Poisson point processes. The results show a good level adjustment and high power values, the latter increasing with the sample size and the discrepancy between the two generating intensities. Finally, these methods have also been applied to the analysis of traffic accidents in Rio de Janeiro (Brazil), studying their distribution at different rush hours.

The paper proposes a new test for detecting the umbrella pattern under a general non-parametric scheme. The alternative asserts that the umbrella ordering holds while the hypothesis is its complement. The main focus is put on controlling the power function of the test outside the alternative. As a result, the asymptotic error of the first kind of the constructed solution is smaller than or equal to the fixed significance level α on the whole set where the umbrella ordering does not hold. Also, under finite sample sizes, this error is controlled to a satisfactory extent. A simulation study shows, among other things, that the new test improves upon the solution widely recommended in the literature of the subject. A routine, written in R, is attached as the Supporting Information file.

We study a new Cramér-von Mises type test for the general nonparametric two-sample problem on the nonnegative half-line. The test statistic is based on the empirical Hankel transforms of the sample variables, critical values are obtained by bootstrapping. The test is shown to be consistent against each fixed alternative. A scale invariant version of the test is also considered. A power comparison with the classical Cramér-von Mises test and another new Cramér-von Mises type test is done by simulation.

This paper deals with the classical statistical problem of comparing the probability distributions of two real random variables X and X 0 , from a double independent sample. While most of the usual tools are based on the cumulative distribution functions F and F 0 of the variables, we focus on the relative density, a function recently used in two-sample problems, and defined as the density of the variable F 0 ( X ) . We provide a nonparametric adaptive strategy to estimate the target function. We first define a collection of estimates using a projection on the trigonometric basis and a preliminary estimator of F 0 . An estimator is selected among this collection of projection estimates, with a criterion in the spirit of the Goldenshluger–Lepski methodology. We show the optimality of the procedure both in the oracle and the minimax sense: the convergence rate for the risk computed from an oracle inequality matches with the lower bound that we also derived. Finally, some simulations illustrate the method.