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How Contagious Is Ebola? More than 200 people are suspected to have died in Ebola outbreaks in the Democratic Republic of the Congo and Uganda , according to the latest figures published by the Centers for Disease Control and Prevention on May 29. The vast majority of these are in the DRC. With no vaccine available for this strain, the World Health Organization declared a public health emergency of international concern on May 1....
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Britain's Nuclear Renaissance Faces Mounting Cost Pressures Authored by Felicity Bradstock via OilPrice.com, Sizewell C and Hinkley Point C are expected to play a major role in expanding Britain’s nuclear generation capacity and reducing dependence on fossil fuels. Both projects have faced concerns over delays and rising costs, with Hinkley Point C’s estimated price nearly doubling from its original forecast. The U.K.....
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I love this update, the pure passion of teenage love is so wonderful to behold. Hopefully there will...
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arXiv:2606.00128v1 Announce Type: new Abstract: Each year the American Statistical Association (ASA) hosts the Annual Data Challenge Expo, which tasks participants with analyzing a given dataset and presenting their work at the Joint Statistical Meeting (JSM). The 2025 Data Challenge Expo tasked participants with analyzing over 35 years of commercial flight data from the United States Bureau of Transportation Statistics (BTS). These data p....
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arXiv:2606.00157v1 Announce Type: new Abstract: We consider establishing the interpretability theory of deep learning through constructing a corresponding relationship between the renormalization group (RG) method in statistical physics and the training process of deep neural networks (DNNs). We have proved the constructed relationship using the one-dimensional Ising model as the input data. In this paper we generalize our results to the c....
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arXiv:2606.00181v1 Announce Type: new Abstract: We introduce a novel regression framework designed to model non-linear responses situated on a sphere $\mathbb{S}$ of finite or infinite dimension. Unlike traditional tangent-space regressions, which lift responses to a tangent space $T_o \mathbb{S}$ and thereby violate intrinsic spherical distances, our proposed method employs an intrinsic approach. We model the conditional mean through an i....
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On Asymptotic Outlier Rejection in Bayesian Mixed Poisson Regression Models Under Extreme Target and Covariate Values
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arxiv.org
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1 month ago
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eng
arXiv:2606.00231v1 Announce Type: new Abstract: Bayesian models are claimed to be fully robust against outliers if, asymptotically, observations infinitely far from the other data do not influence the posterior. Early works in robust Bayesian inference concentrated on continuous distributions and i.i.d. observations. Robustness results were then extended to linear regression in the presence of infinite residuals, either through an outlying....
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arXiv:2606.00233v1 Announce Type: new Abstract: Density estimation is often presented as a choice among parametric summaries, finite mixtures, and nonparametric smoothers. This review argues for a complementary view: a data set can be studied through a path of densities indexed by smoothing scale, diffusion time, model complexity, density level, or noise level. We call this perspective density evolution. Under this lens, Gaussian kernel de....
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Out-of-Distribution generalization of quantile regression with heavy tailed inputs: an SVM approach
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arxiv.org
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1 month ago
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eng
arXiv:2606.00265v1 Announce Type: new Abstract: We study quantile regression in an extrapolation regime where the covariate takes unusually large values. Under regular variation assumptions, extreme observations can be effectively characterized through their angular components, enabling learning strategies that focus on the angle of the most extreme observations. This approach is formalized through the minimization of an asymptotic conditi....
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arXiv:2606.00296v1 Announce Type: new Abstract: Neural operators are often reported to exhibit zero-shot super-resolution, a phenomenon in which a model trained on coarse grids produces accurate predictions on finer testing grids without additional retraining. Despite strong empirical evidence, the theoretical foundations of this phenomenon remain unclear. In this work, we provide a systematic theoretical study of zero-shot super-resolutio..
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arXiv:2606.00302v1 Announce Type: new Abstract: Despite being ubiquitous in science, clustering remains a technique whose results are not quantitatively scrutinized via a framework. We present an analysis called evaluating replicability via iterative clustering assignments (ERICA) that is applied to a dataset to determine whether clusters are identified in a replicable manner. The pipeline computes a statistic that describes whether struct..
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Cluster Analysis with Resampling for Validation and Exploration (CARVE)
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arxiv.org
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1 month ago
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eng
arXiv:2606.00327v1 Announce Type: new Abstract: Clustering is widely used across the sciences as the foundation for downstream data-driven scientific discoveries. However, clustering results are highly sensitive to the choice of algorithm, preprocessing, and the number of clusters $k$, producing scientific claims that are often not reproducible. The current state of the art for validating clustering solutions consists of clustering validat....
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arXiv:2606.00343v1 Announce Type: new Abstract: Motivated by the analysis of the behaviour of extremes from multivariate heavy-tailed distributions, we introduce a novel notion of statistical depth, referred to as Polar Depth. The polar depth function is naturally expressed in polar coordinates, as is the limiting distribution of a regularly varying random variable, beyond asymptotically large thresholds, once its marginals have been appro....
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arXiv:2606.00346v1 Announce Type: new Abstract: Phenomena such as epidemiological processes, hydrologic systems, social platforms, utility services, and supply chains can be represented as topological networks. A central question about these networks concerns connectivity and the permeability of edges. Dyadic regression and related approaches have been proposed to identify network features associated with pairwise node-level differences. I....
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A Distribution-Free Framework for Rewrite-Based Human-text Detection via Knockoff Filtering
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arxiv.org
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1 month ago
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eng
arXiv:2606.00402v1 Announce Type: new Abstract: We propose a distribution-free statistical framework that converts arbitrary rewrite-based detectors into detectors with finite-sample FDR guarantees without retraining. Our key observation is that rewrite-based detection implicitly constructs knockoff samples, enabling LLM-generated text detection to be formulated as a multiple hypothesis testing problem with knockoff structure. This perspec..
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Riemannian Stochastic Optimization for Sufficient Dimension Reduction
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arxiv.org
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1 month ago
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eng
arXiv:2606.00413v1 Announce Type: new Abstract: Sufficient dimension reduction (SDR) makes high-dimensional regression tractable by projecting the covariates onto a low-dimensional subspace that preserves the conditional mean of the response. Existing gradient-based estimators either operate in the ambient space and suffer from the curse of dimensionality, or localize in the reduced space at a per-outer-iteration cost at least quadratic in....
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arXiv:2606.00419v1 Announce Type: new Abstract: Uncertainty quantification (UQ) is critical for the deployment of machine learning predictors in real-world scenarios where the data distribution may shift over time (i.e., data may not be exchangeable). Online conformal prediction (OCP) methods address this issue at the expense of either (i) group-wise error control or (ii) learning-rate independent implementation. Group-conditional coverage....
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arXiv:2606.00425v1 Announce Type: new Abstract: Moment conditions are widely used to identify parameters in models where the full likelihood is either unknown or intentionally left unspecified. Empirical likelihood methods address this problem by assigning probability weights to the observed data so that the sample moment conditions hold exactly. Building on this idea, we propose a nonparametric Bayesian framework based on exponentially ti....
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arXiv:2606.00436v1 Announce Type: new Abstract: Clustering is a central tool for discovering latent structure in unlabeled data; yet modern clustering pipelines often end with a hard assignment of each observation to a cluster without rigorous measures of assignment uncertainty. We propose a novel weighted conformal approach for constructing valid confidence sets for cluster labels. The key difficulty is that the labels available for calib....
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arXiv:2606.00465v1 Announce Type: new Abstract: One investigates the extrinsic statistical analysis on the space of Billera- Holmes-Vogtmann tree space with four leaves (T4 or BHV4) based on its recently proposed novel representation (see [1])- the Spiky Projective ExcavatedDodecahedron (SPED). Due to the symmetry of the SPED, the Veronese- Whitney (VW) embeddingwe consider here produces a natural extrinsicmetric for a statistical analysis..
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arXiv:2606.00478v1 Announce Type: new Abstract: Online high-dimensional regression has gained increasing attention in recent years, yet existing methods typically assume that all candidate features, including important ones, are observed from the outset of data collection. This assumption is often violated in real-world scenarios, where new variables become available gradually as data accumulate. To address this gap, we introduce a novel f....
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When Do Generalized Permutation Tests Achieve Optimal Power? A Dispersion Characterization
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arxiv.org
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1 month ago
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eng
arXiv:2606.00578v1 Announce Type: new Abstract: We study generalized Monte Carlo permutation tests under a non-uniform distribution on permutations. Focusing on the difference-in-means statistic, we introduce two scalar dispersion measures that quantify departures from complete randomization at the individual and pairwise levels. We show that if both dispersions vanish asymptotically, then the conditional permutation distribution converges..
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arXiv:2606.00584v1 Announce Type: new Abstract: This paper proposes Spectra-Guided Neural Tucker Factorization (SG-NTF) for High-Dimensional and Incomplete (HDI) tensor completion. Circumventing discrete representational limits, SG-NTF maps scalar timestamps into a continuous spectral space to abstract temporal periodicities. Concurrently, a Spatio-Temporal Co-Gating (STCG) mechanism explicitly filters latent interactions via multiplicativ..
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Taming the Loss Landscape of PINNs with Noisy Feynman-Kac Supervision: Operator Preconditioning and Non-Asymptotic Error Bounds
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arxiv.org
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1 month ago
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eng
arXiv:2606.00643v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) often train slowly or fail to converge on challenging partial differential equations (PDEs), a behavior recently linked to severely ill-conditioned loss landscapes inherited from the underlying differential operator. We study PINNs augmented with a pointwise data-fidelity term, added at a few points in the domain to the standard residual and boundary l....
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arXiv:2606.00661v1 Announce Type: new Abstract: We establish the finite-sample concentration rate for the Median-of-Incomplete-U-Statistics (MIU), an efficient robust estimator for the expectation of symmetric kernels.
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arXiv:2606.00715v1 Announce Type: new Abstract: We study boundary detection for unlabeled noisy images from a statistical perspective. The aim is to recover an unknown object region from raw intensity observations without pixel-wise annotating labels or a parametric model for the intensity distributions. Motivated by robust Gibbs posterior approaches based on thresholded misclassification losses, we propose a continuous hinge-type surrogat....
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arXiv:2606.00754v1 Announce Type: new Abstract: We introduce causal density functions: Radon-Nikodym derivatives that compare interventional laws to observational laws and therefore act as local density ratios for causal effects. Whereas many causal-strength measures compare whole distributions after graph surgery, causal density functions provide a pointwise change-of-measure object that can be estimated, calibrated, and used to score dir..
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arXiv:2606.00758v1 Announce Type: new Abstract: In recent years, graph signal processing has emerged as a powerful framework at the intersection of signal processing and graph theory, providing tools for the analysis of signals defined on nodes while accounting for their relationships represented by edges. These tools have been successfully applied to various settings, including statistical hypothesis testing. In particular, non-parametric....
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The Effect of Choice of Metric and Scan Length on Reliability in Resting-State fMRI
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arxiv.org
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1 month ago
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eng
arXiv:2606.00767v1 Announce Type: new Abstract: Resting-state fMRI (rs-fMRI) is widely used to investigate brain functional connectivity, but the reliability of these measurements remains a key concern for ensuring reproducibility. The distance-based intraclass correlation coefficient (dbICC) generalizes classical ICC to more general data types, making it well-suited for assessing the reliability of measures of functional connectivity. In ....
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Bayesian Inference of Nonlinear Malaria Dynamics in Ghana via an Ensemble Markov Chain Monte Carlo Sampler
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arxiv.org
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1 month ago
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eng
arXiv:2606.00783v1 Announce Type: new Abstract: Reliable quantification of malaria dynamics in sub-Saharan Africa is hindered by short, noisy, and spatially heterogeneous surveillance records. In Ghana, health-facility data from 2014 to 2023 reveal non-linear and age-specific fluctuations in hospital admissions, yet existing approaches struggle to capture stochastic variability or provide credible uncertainty bounds. This study develops a ....
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