Tootfinder

Diffusion-Based Hypothesis Testing and Change-Point Detection
Sean Moushegian, Taposh Banerjee, Vahid Tarokh
https://arxiv.org/abs/2506.16089 https://

Diffusion-Based Hypothesis Testing and Change-Point Detection
Score-based methods have recently seen increasing popularity in modeling and generation. Methods have been constructed to perform hypothesis testing and change-point detection with score functions, but these methods are in general not as powerful as their likelihood-based peers. Recent works consider generalizing the score-based Fisher divergence into a diffusion-divergence by transforming score functions via multiplication with a matrix-valued function or a weight matrix. In this paper, we ext…

Testing Separability of High-Dimensional Covariance Matrices
Bongjung Sung, Peter D. Hoff
https://arxiv.org/abs/2506.17463 https://ar…

Testing Separability of High-Dimensional Covariance Matrices
Due to their parsimony, separable covariance models have been popular in modeling matrix-variate data. However, the inference from such a model may be misleading if the population covariance matrix $Σ$ is actually non-separable, motivating the use of statistical tests of separability. Likelihood ratio tests have tractable null distributions and good power when the sample size $n$ is larger than the number of variables $p$, but are not well-defined otherwise. Other existing separability tests f…

Testing for large-dimensional covariance matrix under differential privacy
Shiwei Sang, Yicheng Zeng, Xuehu Zhu, Shurong Zheng
https://arxiv.org/abs/2506.02410

Testing for large-dimensional covariance matrix under differential privacy
The increasing prevalence of high-dimensional data across various applications has raised significant privacy concerns in statistical inference. In this paper, we propose a differentially private integrated statistic for testing large-dimensional covariance structures, enabling accurate statistical insights while safeguarding privacy. First, we analyze the global sensitivity of sample eigenvalues for sub-Gaussian populations, where our method bypasses the commonly assumed boundedness of data co…

Coupled integral equations method with open boundary conditions for calculation the characteristics of structured waveguides
M. I. Ayzatsky
https://arxiv.org/abs/2505.13086

Coupled integral equations method with open boundary conditions for calculation the characteristics of structured waveguides
The results of modification of the CASCIE code aimed at implementing open boundary conditions are presented. The accelerator section developed at CERN was chosen as a prototype for the structured waveguide under testing. Results of testing the CASCIE-M code confirms that the implementation of matrix open boundary conditions gives possibility to consider the structure in which waves enter and exit without additional reflections from couplers. It was shown that the dependence of the reflection co…

Alice and the Caterpillar: A more descriptive null model for assessing data mining results
Giulia Preti, Gianmarco De Francisci Morales, Matteo Riondato
https://arxiv.org/abs/2506.09764

Alice and the Caterpillar: A more descriptive null model for assessing data mining results
We introduce novel null models for assessing the results obtained from observed binary transactional and sequence datasets, using statistical hypothesis testing. Our null models maintain more properties of the observed dataset than existing ones. Specifically, they preserve the Bipartite Joint Degree Matrix of the bipartite (multi-)graph corresponding to the dataset, which ensures that the number of caterpillars, i.e., paths of length three, is preserved, in addition to other properties conside…

Testing T2K's Bayesian constraints with priors in alternate parameterisations
Collaboration, Abe, Abe, Akutsu, Alarakia-Charles, Hakim, Monsalve, Anthony, Aoki, Apte, Arai, Arihara, Arimoto, Ashida, Atkin, Babu, Baranov, Barker, Barr, Barrow, Bates, Bathe-Peters, Batkiewicz-Kwasniak, Baudis, Berardi, Berns, Bhattacharjee, Blanchet, Blondel, Boistier, Bolognesi, Bordoni, Boyd, Bronner, Bubak, Avanzini, Caballero, Cadoux, Calabria, Cao, Cap, Carabadjac, Cartwright, Casado, Catanesi, …

Testing T2K's Bayesian constraints with priors in alternate parameterisations
Bayesian analysis results require a choice of prior distribution. In long-baseline neutrino oscillation physics, the usual parameterisation of the mixing matrix induces a prior that privileges certain neutrino mass and flavour state symmetries. Here we study the effect of privileging alternate symmetries on the results of the T2K experiment. We find that constraints on the level of CP violation (as given by the Jarlskog invariant) are robust under the choices of prior considered in the analysis…

Testing Hypotheses of Covariate Effects on Topics of Discourse
Gabriel Phelan, David A. Campbell
https://arxiv.org/abs/2506.05570 https://

Testing Hypotheses of Covariate Effects on Topics of Discourse
We introduce an approach to topic modelling with document-level covariates that remains tractable in the face of large text corpora. This is achieved by de-emphasizing the role of parameter estimation in an underlying probabilistic model, assuming instead that the data come from a fixed but unknown distribution whose statistical functionals are of interest. We propose combining a convex formulation of non-negative matrix factorization with standard regression techniques as a fast-to-compute and…

Second-order prestress stability and third-order rigidity of polyhedral surfaces
Zeyuan He
https://arxiv.org/abs/2506.22019 https://a…

Second-order prestress stability and third-order rigidity of polyhedral surfaces
The energy-based definition provides a viable resolution to the longstanding confusion on the proper definition of $n$-th order rigidity and flexibility in geometric constraint systems. Applying an energy-based local rigidity analysis to polyhedral surfaces, we derive new criterion for testing second-order prestress stability. Additionally, we discuss the limitations of testing third-order rigidity, except in the special case where the null space of the rigidity matrix is one-dimensional. Altho…