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Assessing whether two patient populations exhibit comparable event dynamics is essential for evaluating treatment equivalence, pooling data across cohorts, or comparing clinical pathways across hospitals or strategies. We introduce a…

Methodology · Statistics 2026-04-10 Zoe Kristin Lange , Maryam Farhadizadeh , Holger Dette , Nadine Binder

The bootstrap is a method for estimating the distribution of an estimator or test statistic by re-sampling the data or a model estimated from the data. Under conditions that hold in a wide variety of econometric applications, the bootstrap…

Econometrics · Economics 2018-09-12 Joel L. Horowitz

We consider the problem of testing a null hypothesis defined by equality and inequality constraints on a statistical parameter. Testing such hypotheses can be challenging because the number of relevant constraints may be on the same order…

Methodology · Statistics 2024-02-19 Nils Sturma , Mathias Drton , Dennis Leung

This paper addresses the problem of deciding whether the dose response relationships between subgroups and the full population in a multi-regional trial are similar to each other. Similarity is measured in terms of the maximal deviation…

Statistics Theory · Mathematics 2024-04-30 Holger Dette , Lukas Koletzko , Frank Bretz

This paper considers the problem of testing the equality of two unspecified distributions. The classical omnibus tests such as the Kolmogorov-Smirnov and Cram\`er-von Mises are known to suffer from low power against essentially all but…

Statistics Theory · Mathematics 2015-09-15 Wen-Xin Zhou , Chao Zheng , Zhen Zhang

The problem of testing the equality of the generating processes of two categorical time series is addressed in this work. To this aim, we propose three tests relying on a dissimilarity measure between categorical processes. Particular…

Methodology · Statistics 2023-05-02 Ángel López-Oriona , José Antonio Vilar Fernández , Pierpaolo D'Urso

This paper studies the matched network inference problem, where the goal is to determine if two networks, defined on a common set of nodes, exhibit a specific form of stochastic similarity. Two notions of similarity are considered: (i)…

Methodology · Statistics 2025-02-06 Somnath Bhadra , Kaustav Chakraborty , Srijan Sengupta , Soumendra Lahiri

In the analysis of microarray data, and in some other contemporary statistical problems, it is not uncommon to apply hypothesis tests in a highly simultaneous way. The number, $\nu$ say, of tests used can be much larger than the sample…

Statistics Theory · Mathematics 2007-06-13 J. Fan , P. Hall , Q. Yao

In clinical trials the comparison of two different populations is a frequently addressed problem. Non-linear (parametric) regression models are commonly used to describe the relationship between covariates as the dose and a response…

Methodology · Statistics 2019-02-12 Kathrin Möllenhoff , Frank Bretz , Holger Dette

We study the problem of testing the equivalence of functional parameters (such as the mean or variance function) in the two sample functional data problem. In contrast to previous work, which reduces the functional problem to a multiple…

Statistics Theory · Mathematics 2020-04-28 Holger Dette , Kevin Kokot

This paper is an attempt to set a justification for making use of some dicrepancy indexes, starting from the classical Maximum Likelihood definition, and adapting the corresponding basic principle of inference to situations where…

Statistics Theory · Mathematics 2021-02-24 Michel Broniatowski

A Poisson Binomial distribution over $n$ variables is the distribution of the sum of $n$ independent Bernoullis. We provide a sample near-optimal algorithm for testing whether a distribution $P$ supported on $\{0,...,n\}$ to which we have…

Data Structures and Algorithms · Computer Science 2014-10-15 Jayadev Acharya , Constantinos Daskalakis

We provide a distribution-free test that can be used to determine whether any two joint distributions $p$ and $q$ are statistically different by inspection of a large enough set of samples. Following recent efforts from Long et al. [1], we…

Machine Learning · Computer Science 2016-07-26 Francesco Solera , Andrea Palazzi

The bivariate Poisson distribution is commonly used to model bivariate count data. In this paper we study a goodness-of-fit test for this distribution. We also provide a review of the existing tests for the bivariate Poisson distribution,…

Statistics Theory · Mathematics 2019-02-26 Francisco Novoa-Muñoz

We say that two probabilities are similar at level $\alpha$ if they are contaminated versions (up to an $\alpha$ fraction) of the same common probability. We show how this model is related to minimal distances between sets of trimmed…

Statistics Theory · Mathematics 2012-05-10 Pedro C. Álvarez-Esteban , Eustasio del Barrio , Juan A. Cuesta-Albertos , Carlos Matrán

Meta-analysis combines pertinent information from existing studies to provide an overall estimate of population parameters/effect sizes, as well as to quantify and explain the differences between studies. However, testing the between-study…

Methodology · Statistics 2020-11-13 Han Du , Ge Jiang , Zijun Ke

Recently Liu and Wang derived the likelihood ratio test (LRT) statistic and its asymptotic distribution for testing equality of two multinomial distributions vs. the alternative that the second distribution is larger in terms of increasing…

Statistics Theory · Mathematics 2007-06-13 Arthur Cohen , John Kolassa , Harold Sackrowitz

The robust rank-order test (Fligner and Policello, 1981) was designed as an improvement of the non-parametric Wilcoxon-Mann-Whitney U-test to be more appropriate when the samples being compared have unequal variance. However, it tends to be…

Methodology · Statistics 2020-09-08 Nirvik Sinha

``Behind every limit theorem, there is an inequality'' said Kolmogorov. We say ``for every inequality, there is an approximate inequality under approximate regularity conditions.'' Suppose $X, X'$ are independent and identically distributed…

Statistics Theory · Mathematics 2026-04-17 Manit Paul , Arun Kumar Kuchibhotla

Testing for normality is a widely used procedure in statistics and data analysis, often applied prior to employing methods that rely on the assumption of normally distributed data. While several existing tests target distributional…

Methodology · Statistics 2026-04-07 Akin Anarat , Holger Schwender
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