Tootfinder

The fastverse is a suite of complementary high-performance packages for statistical computing and data manipulation in R. #rstats

GitHub - fastverse/fastverse: An Extensible Suite of High-Performance and Low-Dependency Packages for Statistical Computing and Data Manipulation in R
An Extensible Suite of High-Performance and Low-Dependency Packages for Statistical Computing and Data Manipulation in R - fastverse/fastverse

T-Detect: Tail-Aware Statistical Normalization for Robust Detection of Adversarial Machine-Generated Text
Alva West, Luodan Zhang, Liuliu Zhang, Minjun Zhu, Yixuan Weng, Yue Zhang
https://arxiv.org/abs/2507.23577

T-Detect: Tail-Aware Statistical Normalization for Robust Detection of Adversarial Machine-Generated Text
The proliferation of sophisticated text generation models necessitates the development of robust detection methods capable of identifying machine-generated content, particularly text designed to evade detection through adversarial perturbations. Existing zero-shot detectors often rely on statistical measures that implicitly assume Gaussian distributions, a premise that falters when confronted with the heavy-tailed statistical artifacts characteristic of adversarial or non-native English texts. …

Statistical Invisibility of a Physical Attack on QRNGs After Randomness Extraction
Yifan Chen, Dong Wang, Yibo Zhao, Liang Cheng, Yi Zhang, Yang Zhang
https://arxiv.org/abs/2508.21498

Statistical Invisibility of a Physical Attack on QRNGs After Randomness Extraction
Current prevailing designs of quantum random number generators (QRNGs) designs typically employ post-processing techniques to distill raw random data, followed by statistical verification with suites like NIST SP 800-22. This paper demonstrates that this widely adopted practice harbors a critical flaw. We show that the powerful extraction process can create a false sense of security by perfectly concealing physical-layer attacks, rendering the subsequent statistical tests blind to a compromised…

\phi-Sectional curvature of statistical structures on almost contact metric manifolds
Abbas Heydari, Sadegh Mohammadi
https://arxiv.org/abs/2507.00811 http…

ϕ-Sectional curvature of statistical structures on almost contact metric manifolds
In this article, we study and analyze the ϕ-sectional curvature induced by a statistical structure on an almost contact metric manifold. We demonstrate that this sectional curvature is always non-positive. Additionally, we present equivalent statements regarding the vanishing of this type of sectional curvature. Furthermore, we derive a sufficient condition for an almost contact statistical manifold to be classified as a cosymplectic statistical manifold.

Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence
Andr\'as Gy\"orgy, Tor Lattimore, Nevena Lazi\'c, Csaba Szepesv\'ari
https://arxiv.org/abs/2506.23908

Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence
Sound deductive reasoning -- the ability to derive new knowledge from existing facts and rules -- is an indisputably desirable aspect of general intelligence. Despite the major advances of AI systems in areas such as math and science, especially since the introduction of transformer architectures, it is well-documented that even the most advanced frontier systems regularly and consistently falter on easily-solvable deductive reasoning tasks. Hence, these systems are unfit to fulfill the dream o…

A simulation study comparing statistical approaches for estimating extreme quantile regression with an application to forecasting of fire risk
Amina El Bernoussi, Mohamed El Arrouchi
https://arxiv.org/abs/2506.23161

A simulation study comparing statistical approaches for estimating extreme quantile regression with an application to forecasting of fire risk
This simulation study compares statistical approaches for estimating extreme quantile regression, with a specific application to fire risk forecasting. A simulation-based framework is designed to evaluate the effectiveness of different methods in capturing extreme dependence structures and accurately predicting extreme quantiles. These approaches are applied to fire occurrence data from the Fez-Meknes region, where a positive relationship is observed between increasing maximum temperatures and …

Chance-Constrained DC Optimal Power Flow Using Constraint-Informed Statistical Estimation
Tianyang Yi, D. Adrian Maldonado, Anirudh Subramanyam
https://arxiv.org/abs/2508.21687 …

Chance-Constrained DC Optimal Power Flow Using Constraint-Informed Statistical Estimation
Chance-constrained optimization has emerged as a promising framework for managing uncertainties in power systems. This work advances its application to the DC Optimal Power Flow (DC-OPF) model, developing a novel approach to uncertainty modeling and estimation. Current methods typically tackle these problems by first modeling random nodal injections using high-dimensional statistical distributions that scale with the number of buses, followed by deriving deterministic reformulations of the prob…

A Statistical Physics perspective on fairness in shared expenses: The bar bill analogy
Nuno Crokidakis, Lucas Sigaud
https://arxiv.org/abs/2507.23542 https://

A Statistical Physics perspective on fairness in shared expenses: The bar bill analogy
In social contexts where individuals consume varying amounts, such as shared meals or bar gatherings, splitting the total bill equally often yields surprisingly fair outcomes. In this work, we develop a statistical physics framework to explain this emergent fairness by modeling individual consumption as stochastic variables drawn from a realistic distribution, specifically the gamma distribution. Introducing a Boltzmann-like weighting factor, we derive exact analytical expressions for the parti…

Quantum-inspired probability metrics define a complete, universal space for statistical learning
Logan S. McCarty
https://arxiv.org/abs/2508.21086 https://…

Quantum-inspired probability metrics define a complete, universal space for statistical learning
Comparing probability distributions is a core challenge across the natural, social, and computational sciences. Existing methods, such as Maximum Mean Discrepancy (MMD), struggle in high-dimensional and non-compact domains. Here we introduce quantum probability metrics (QPMs), derived by embedding probability measures in the space of quantum states: positive, unit-trace operators on a Hilbert space. This construction extends kernel-based methods and overcomes the incompleteness of MMD on non-co…

Cyber Attacks Detection, Prevention, and Source Localization in Digital Substation Communication using Hybrid Statistical-Deep Learning
Nicola Cibin, Bas Mulder, Herman Carstens, Peter Palensky, Alexandru \c{S}tefanov
https://arxiv.org/abs/2507.00522

Cyber Attacks Detection, Prevention, and Source Localization in Digital Substation Communication using Hybrid Statistical-Deep Learning
The digital transformation of power systems is accelerating the adoption of IEC 61850 standard. However, its communication protocols, including Sampled Values (SV), lack built-in security features such as authentication and encryption, making them vulnerable to malicious packet injection. Such cyber attacks can delay fault clearance or trigger unintended circuit breaker operations. While most existing research focuses on detecting cyber attacks in digital substations, intrusion prevention syste…

What if we find nothing? Bayesian analysis of the statistical information of null results in future exoplanet habitability and #biosignature surveys: https://arxiv.org/abs/2504.06779 -> interview with the first author: https://www.youtube.com/watch?v=FbnmxiTlBUo

Wireless AI Evolution: From Statistical Learners to Electromagnetic-Guided Foundation Models
Jian Xiao, Ji Wang, Kunrui Cao, Xingwang Li, Zhao Chen, Chau Yuen
https://arxiv.org/abs/2507.00366

Wireless AI Evolution: From Statistical Learners to Electromagnetic-Guided Foundation Models
While initial applications of artificial intelligence (AI) in wireless communications over the past decade have demonstrated considerable potential using specialized models for targeted communication tasks, the revolutionary demands of sixth-generation (6G) networks for holographic communications, ubiquitous sensing, and native intelligence are propelling a necessary evolution towards AI-native wireless networks. The arrival of large AI models paves the way for the next phase of Wireless AI, dr…

A dynamical coherency gate for state recovery: Statistical requiem for the long arc of cislunar orbital mis-prediction
Binyamin Stivi, Vishnuu Mallik, Lamberto Dell'Elce, Aaron J. Rosengren
https://arxiv.org/abs/2506.22748

A dynamical coherency gate for state recovery: Statistical requiem for the long arc of cislunar orbital mis-prediction
We present a statistically grounded methodology for recovering physically consistent initial conditions from historical two-line element sets (TLEs), enabling accurate long-arc trajectory reconstruction for distant and highly eccentric Earth satellites. The approach combines numerical averaging of osculating orbital elements with Gaussian-mixture-model (GMM) filtering with robust fallback strategies to isolate a dynamically coherent subset of mean elements at a common reference epoch. From this…

Automated Test Data Generation for Enterprise Protobuf Systems: A Metaclass-Enhanced Statistical Approach
Y. Du
https://arxiv.org/abs/2507.22070 https://ar…

Automated Test Data Generation for Enterprise Protobuf Systems: A Metaclass-Enhanced Statistical Approach
Large-scale enterprise systems utilizing Protocol Buffers (protobuf) present significant challenges for performance testing, particularly when targeting intermediate business interfaces with complex nested data structures. Traditional test data generation approaches are inadequate for handling the intricate hierarchical and graph-like structures inherent in enterprise protobuf schemas. This paper presents a novel test data generation framework that leverages Python's metaclass system for dynami…

Replaced article(s) found for cond-mat.stat-mech. https://arxiv.org/list/cond-mat.stat-mech/new
[1/1]:
- Langevin equations and a geometric integration scheme for the overdamped limit of rotational Brow...
Felix H\"ofling, Arthur V. Straube

Hybrid Cryptographic Monitoring System for Side-Channel Attack Detection on PYNQ SoCs
Nishant Chinnasami, Rasha Karakchi
https://arxiv.org/abs/2508.21606 https://

Hybrid Cryptographic Monitoring System for Side-Channel Attack Detection on PYNQ SoCs
AES-128 encryption is theoretically secure but vulnerable in practical deployments due to timing and fault injection attacks on embedded systems. This work presents a lightweight dual-detection framework combining statistical thresholding and machine learning (ML) for real-time anomaly detection. By simulating anomalies via delays and ciphertext corruption, we collect timing and data features to evaluate two strategies: (1) a statistical threshold method based on execution time and (2) a Random…

A Statistical Analysis of Magnetic Parameters in Solar Source Regions of Halo-CMEs with and without SEP events
Xuchun Duan, Ting Li, Yijun Hou, Yang Wang, Yue Li, Yining Zhang, Zheng Sun, Guiping Zhou
https://arxiv.org/abs/2506.21907

A Statistical Analysis of Magnetic Parameters in Solar Source Regions of Halo-CMEs with and without SEP events
Large SEPs can cause adverse space weather hazard to humans technology and such events are especially associated with halo coronal mass ejections (CMEs). But in turn, a significant portion of halo-CMEs are not associated with large SEPs. The objective of this study is to gain an understanding of the source region distinctions between halo-CMEs in SEP and No-SEP events. Among the 176 halo-CMEs observed from 2010-2024, we screen out 45 large SEP events and 131 No-SEP events from this dataset. It …

A visual analytics tool for taxonomy-based trajectory data exploration
Ivan A. Hanono Cozzetti, Ahmad Abdou
https://arxiv.org/abs/2507.22899 https://arxiv.…

A visual analytics tool for taxonomy-based trajectory data exploration
The analysis of spatio-temporal data presents significant challenges due to the complexity and heterogeneity of movement patterns. This project proposes a data analytics tool that combines data visualization and statistical computation to facilitate spatio-temporal data analysis through a multi-level approach. The tool categorizes moving objects into distinct taxonomies using Machine Learning models, adding meaningful structure to the analysis. Two case studies demonstrate the methodology's eff…

Multi-Agent Path Finding Among Dynamic Uncontrollable Agents with Statistical Safety Guarantees
Kegan J. Strawn, Thomy Phan, Eric Wang, Nora Ayanian, Sven Koenig, Lars Lindemann
https://arxiv.org/abs/2507.22282

Multi-Agent Path Finding Among Dynamic Uncontrollable Agents with Statistical Safety Guarantees
Existing multi-agent path finding (MAPF) solvers do not account for uncertain behavior of uncontrollable agents. We present a novel variant of Enhanced Conflict-Based Search (ECBS), for both one-shot and lifelong MAPF in dynamic environments with uncontrollable agents. Our method consists of (1) training a learned predictor for the movement of uncontrollable agents, (2) quantifying the prediction error using conformal prediction (CP), a tool for statistical uncertainty quantification, and (3) i…

Diffusion model for gradient preconditioning in hyperspectral imaging inverse problems
Jonathan Monsalve, Kumar Vijay Mishra
https://arxiv.org/abs/2507.23065 https://

Diffusion model for gradient preconditioning in hyperspectral imaging inverse problems
Recovering high-dimensional statistical structure from limited measurements is a fundamental challenge in hyperspectral imaging, where capturing full-resolution data is often infeasible due to sensor, bandwidth, or acquisition constraints. A common workaround is to partition measurements and estimate local statistics-such as the covariance matrix-using only partial observations. However, this strategy introduces noise in the optimization gradients, especially when each partition contains few sa…

Replaced article(s) found for q-fin.ST. https://arxiv.org/list/q-fin.ST/new
[1/1]:
- Mitigating Distribution Shift in Stock Price Data via Return-Volatility Normalization for Accurat...
Hyunwoo Lee, Jihyeong Jeon, Jaemin Hong, U Kang

Large Language Models for Statistical Inference: Context Augmentation with Applications to the Two-Sample Problem and Regression
Marc Ratkovic
https://arxiv.org/abs/2506.23862

Large Language Models for Statistical Inference: Context Augmentation with Applications to the Two-Sample Problem and Regression
We introduce context augmentation, a data-augmentation approach that uses large language models (LLMs) to generate contexts around observed strings as a means of facilitating valid frequentist inference. These generated contexts serve to reintroduce uncertainty, incorporate auxiliary information, and facilitate interpretability. For example, in the two-sample test, we compare the log-probability of strings under contexts from its own versus the other group. We show on synthetic data that the me…

Average-case complexity in statistical inference: A puzzle-driven research seminar
Anastasia Kireeva, Afonso S. Bandeira
https://arxiv.org/abs/2506.22182 h…

Average-case complexity in statistical inference: A puzzle-driven research seminar
These notes describe our experience with running a student seminar on average-case complexity in statistical inference using the jigsaw learning format at ETH Zurich in Fall of 2024. The jigsaw learning technique is an active learning technique where students work in groups on independent parts of the task and then reassemble the groups to combine all the parts together. We implemented this technique for the proofs of various recent research developments, combined with a presentation by one of …

Re-Examining the Statistical Methodology and Onomastic Claims of Gregor and Blais' Argument from Name Popularity
Jason Wilson, Luuk van de Weghe
https://arxiv.org/abs/2508.21150

Re-Examining the Statistical Methodology and Onomastic Claims of Gregor and Blais' Argument from Name Popularity
In 2024 Gregor and Blais published a JSNT article using two different statistical methods to conclude, contra Bauckham (2017), that selected Apocryphal texts and the Babylonian Talmud "do not correspond to the distribution among first-century Palestinian Jews statistically significantly worse than the distribution in Gospels-Acts" and "the two corpora paradoxically align better in some respects". In this paper, we show that the first method is statistically invalid, and the second is the wrong …

Investigating Energy-Dependent Anisotropy in Cosmic Rays with IceTop Surface Array
Rasha Abbasi (for the IceCube Collaboration), Paolo Desiati (for the IceCube Collaboration), Juan Carlos D\'iaz V\'elez (for the IceCube Collaboration), Frank McNally (for the IceCube Collaboration)
https://arxiv.org/abs/2507.23044

Investigating Energy-Dependent Anisotropy in Cosmic Rays with IceTop Surface Array
This study presents preliminary results from the analysis of cosmic-ray anisotropy using air showers detected by the IceTop surface array between 2011 and 2022. With improved statistical precision and updated Monte Carlo simulation events compared to previous IceTop reports, we investigate anisotropy patterns across four energy ranges spanning from 300 TeV to 6.9 PeV. This work extends the measurement of cosmic-ray anisotropy in the southern hemisphere to higher energies than previously achieve…

Modeling the Cosmological Lyman-$\alpha$ Forest at the Field Level
Roger de Belsunce, Mikhail M. Ivanov, James M. Sullivan, Kazuyuki Akitsu, Shi-Fan Chen
https://arxiv.org/abs/2507.00284

Modeling the Cosmological Lyman-$α$ Forest at the Field Level
The distribution of absorption lines in the spectra of distant quasars, called the Lyman-$α$ (Ly-$α$) forest, is a unique probe of cosmology and the intergalactic medium at high redshifts and small scales. The statistical power of ongoing redshift surveys demands precise theoretical tools to model the Ly-$α$ forest. We address this challenge by developing an analytic, perturbative forward model to predict the Ly-$α$ forest at the field level for a given set of cosmological initial condition…

Prediction Gaps as Pathways to Explanation: Rethinking Educational Outcomes through Differences in Model Performance
Javier Garcia-Bernardo, Eva Jaspers, Weverthon Machado, Samuel Plach, Erik Jan van Leeuwen
https://arxiv.org/abs/2506.22993

Prediction Gaps as Pathways to Explanation: Rethinking Educational Outcomes through Differences in Model Performance
Social contexts -- such as families, schools, and neighborhoods -- shape life outcomes. The key question is not simply whether they matter, but rather for whom and under what conditions. Here, we argue that prediction gaps -- differences in predictive performance between statistical models of varying complexity -- offer a pathway for identifying surprising empirical patterns (i.e., not captured by simpler models) which highlight where theories succeed or fall short. Using population-scale admin…

Protein Drift-Diffusion in Membranes with Non-equilibrium Fluctuations arising from Gradients in Concentration or Temperature
D. Jasuja, P. J. Atzberger
https://arxiv.org/abs/2506.22695

Protein Drift-Diffusion in Membranes with Non-equilibrium Fluctuations arising from Gradients in Concentration or Temperature
We investigate proteins within heterogeneous cell membranes where non-equilibrium phenomena arises from spatial variations in concentration and temperature. We develop simulation methods building on non-equilibrium statistical mechanics to obtain stochastic hybrid continuum-discrete descriptions which track individual protein dynamics, spatially varying concentration fluctuations, and thermal exchanges. We investigate biological mechanisms for protein positioning and patterning within membranes…

Dependability Theory-based Statistical QoS Provisioning of Fluid Antenna Systems
Irfan Muhammad, Priyadarshi Mukherjee, Wee Kiat New, Hirley Alves, Ioannis Krikidis, Kai-Kit Wong
https://arxiv.org/abs/2507.19984

Dependability Theory-based Statistical QoS Provisioning of Fluid Antenna Systems
Fluid antenna systems (FAS) have recently emerged as a promising technology for next-generation wireless networks, offering real-time spatial reconfiguration to enhance reliability, throughput, and energy efficiency. Nevertheless, existing studies often overlook the temporal dynamics of channel fading and their implications for mission-critical operations. In this paper, we propose a dependability-theoretic framework for statistical quality-of-service (QoS) provisioning of FAS under finite bloc…

What the Boltzmann money game teaches us about statistical mechanics (and maybe economics)
Dmitrii E. Makarov
https://arxiv.org/abs/2507.22180 https://arxi…

What the Boltzmann money game teaches us about statistical mechanics (and maybe economics)
This note explains why a large class of fair, or reversible "money games", i.e., stochastic models of wealth redistribution among agents, lead to steady states described by canonical and microcanonical distributions. The games considered include, for example, ones where more than two agents can be simultaneously involved in money transfers (similarly to many-body collisions in chemical kinetics) and where amounts transferred between agents are random. At the same time, money games that break ti…

Comparing Misspecified Models with Big Data: A Variational Bayesian Perspective
Yong Li, Sushanta K. Mallick, Tao Zeng, Junxing Zhang
https://arxiv.org/abs/2507.00763

Comparing Misspecified Models with Big Data: A Variational Bayesian Perspective
Optimal data detection in massive multiple-input multiple-output (MIMO) systems often requires prohibitively high computational complexity. A variety of detection algorithms have been proposed in the literature, offering different trade-offs between complexity and detection performance. In recent years, Variational Bayes (VB) has emerged as a widely used method for addressing statistical inference in the context of massive data. This study focuses on misspecified models and examines the risk fu…

Normality and the Turing Test
Alexandre Kabbach
https://arxiv.org/abs/2508.21382 https://arxiv.org/pdf/2508.21382…

Normality and the Turing Test
This paper proposes to revisit the Turing test through the concept of normality. Its core argument is that the statistical interpretation of the normal--understood as the average both in the normative and mathematical sense of the term--proves useful for understanding the Turing test in at least two ways. First, in the sense that the Turing test targets normal/average rather than exceptional human intelligence, so that successfully passing the test requires building machines that "make mistakes…

Fundamental Limitations of Absolute Ranging via Deep Frequency Modulation Interferometry
Miguel Dovale-\'Alvarez
https://arxiv.org/abs/2507.23183 https://

Fundamental Limitations of Absolute Ranging via Deep Frequency Modulation Interferometry
High-precision absolute length metrology using laser interferometry faces inherent challenges from interferometric ambiguity. Deep Frequency Modulation Interferometry (DFMI) addresses this by simultaneously extracting two distinct length-encoding parameters: the precise, ambiguous interferometric phase ($Φ$) and the coarser, unambiguous effective modulation depth ($m$). This paper establishes the fundamental statistical and systematic limitations governing the absolute accuracy of DFMI. We det…

On Universality of Non-Separable Approximate Message Passing Algorithms
Max Lovig, Tianhao Wang, Zhou Fan
https://arxiv.org/abs/2506.23010 https://

On Universality of Non-Separable Approximate Message Passing Algorithms
Mean-field characterizations of first-order iterative algorithms -- including Approximate Message Passing (AMP), stochastic and proximal gradient descent, and Langevin diffusions -- have enabled a precise understanding of learning dynamics in many statistical applications. For algorithms whose non-linearities have a coordinate-separable form, it is known that such characterizations enjoy a degree of universality with respect to the underlying data distribution. However, mean-field characterizat…

Glyph-Based Multiscale Visualization of Turbulent Multi-Physics Statistics
Arisa Cowe, Tyson Neuroth, Qi Wu, Martin Rieth, Jacqueline Chen, Myoungkyu Lee, Kwan-Liu Ma
https://arxiv.org/abs/2506.23092

Glyph-Based Multiscale Visualization of Turbulent Multi-Physics Statistics
Many scientific and engineering problems involving multi-physics span a wide range of scales. Understanding the interactions across these scales is essential for fully comprehending such complex problems. However, visualizing multivariate, multiscale data within an integrated view where correlations across space, scales, and fields are easily perceived remains challenging. To address this, we introduce a novel local spatial statistical visualization of flow fields across multiple fields and tur…

AS2FM: Enabling Statistical Model Checking of ROS 2 Systems for Robust Autonomy
Christian Henkel, Marco Lampacrescia, Michaela Klauck, Matteo Morelli
https://arxiv.org/abs/2508.18820

AS2FM: Enabling Statistical Model Checking of ROS 2 Systems for Robust Autonomy
Designing robotic systems to act autonomously in unforeseen environments is a challenging task. This work presents a novel approach to use formal verification, specifically Statistical Model Checking (SMC), to verify system properties of autonomous robots at design-time. We introduce an extension of the SCXML format, designed to model system components including both Robot Operating System 2 (ROS 2) and Behavior Tree (BT) features. Further, we contribute Autonomous Systems to Formal Models (AS2…

Quantum Sequential Universal Hypothesis Testing
Matteo Zecchin, Osvaldo Simeone, Aaditya Ramdas
https://arxiv.org/abs/2508.21594 https://arxiv.org/pdf/2508…

Quantum Sequential Universal Hypothesis Testing
Quantum hypothesis testing (QHT) concerns the statistical inference of unknown quantum states. In the general setting of composite hypotheses, the goal of QHT is to determine whether an unknown quantum state belongs to one or another of two classes of states based on the measurement of a number of copies of the state. Prior art on QHT with composite hypotheses focused on a fixed-copy two-step protocol, with state estimation followed by an optimized joint measurement. However, this fixed-copy ap…

Explanations are a means to an end
Jessica Hullman, Ziyang Guo, Berk Ustun
https://arxiv.org/abs/2506.22740 https://arxiv.org/pdf/250…

Explanations are a means to an end
Modern methods for explainable machine learning are designed to describe how models map inputs to outputs--without deep consideration of how these explanations will be used in practice. This paper argues that explanations should be designed and evaluated with a specific end in mind. We describe how to formalize this end in a framework based in statistical decision theory. We show how this functionally-grounded approach can be applied across diverse use cases, such as clinical decision support, …

Crosslisted article(s) found for cond-mat.stat-mech. https://arxiv.org/list/cond-mat.stat-mech/new
[1/1]:
- Two-Step Bose-Einstein Condensation of an ideal Magnetized Charged Bosonic gas under neutron star...
Ayon, P\'erez, Martinez, Rojas, Bocchi, Concepcion, Sanch…

On signs of Fourier coefficients on GL(n)
Didier Lesesvre, Ming Ho Ng, Yingnan Wang
https://arxiv.org/abs/2507.22718 https://arxiv.org/pdf/2507.22718

On signs of Fourier coefficients on GL(n)
We study statistical properties of Fourier coefficients of automorphic forms on GL(n). For most Hecke-Maass cusp forms, we give the asymptotic number of nonvanishing coefficients, show that there is a positive proportion of sign changes among them, when these are real, and describe the asymptotic density of these signs. We generalize the results by Jääsaari obtained in the case of self-dual forms of GL(3) and our method moreover circumvents the assumption of the Generalized Ramanujan Conjectu…

Evaluating Ethnic Income Gap in China: The Case of Han, Mongol, and Manchu in Liaoning and Inner Mongolia
Xinyan Deng
https://arxiv.org/abs/2508.21625 https://

Evaluating Ethnic Income Gap in China: The Case of Han, Mongol, and Manchu in Liaoning and Inner Mongolia
This study analyzes the 2018 Chinese Household Income Project survey data to evaluate the income gaps between an "outsider" ethnic minority group, the Mongols, an "insider" ethnic minority group, the Manchus, and the majority Han group in urban and rural areas of Liaoning province and Inner Mongolia in China. Three statistical methods, a simple first-order OLS linear regression, linear regressions with interaction terms, and the Blinder-Oaxaca Decomposition, are used to investigate the income d…

Formal Bayesian Transfer Learning via the Total Risk Prior
Nathan Wycoff, Ali Arab, Lisa O. Singh
https://arxiv.org/abs/2507.23768 https://arxiv.org/pdf/25…

Formal Bayesian Transfer Learning via the Total Risk Prior
In analyses with severe data-limitations, augmenting the target dataset with information from ancillary datasets in the application domain, called source datasets, can lead to significantly improved statistical procedures. However, existing methods for this transfer learning struggle to deal with situations where the source datasets are also limited and not guaranteed to be well-aligned with the target dataset. A typical strategy is to use the empirical loss minimizer on the source data as a pr…

Algorithm for computing the partition function of the Potts model for SP-graphs
Sofya Mukhamedzhanova, Bulat Sabirov, Amir Mukhamedzhanov
https://arxiv.org/abs/2507.22579 https:…

Algorithm for computing the partition function of the Potts model for SP-graphs
The q-state Potts model is a fundamental framework in statistical physics and graph theory, with its partition function encoding rich information about spin configurations. The multivariate Tutte polynomial (known as the partition function of the Potts model) can be defined on an arbitrary finite graph $G$ and encodes a lot of important combinatorial information about the graph. As a special case, it contains the familiar Tutte polynomial with two variables and, consequently, its specialization…

Cluster analysis of earthquake hypocenters in Azerbaijan and surrounding territories
Sergii Skurativskyi, Sergiy Mykulyak, Yuliya Semenova, Kateryna Skurativska
https://arxiv.org/abs/2506.22829

Cluster analysis of earthquake hypocenters in Azerbaijan and surrounding territories
The research focuses on seismic events that occurred in Azerbaijan and adjacent territories, regions known for strong seismic activity. We analyze a catalog of recorded earthquakes between 2010 and 2023, extracting the locations of the earthquake hypocenters for study purposes. Using statistical methods and cluster analysis tools, we developed a procedure for partitioning hypocenter clusters. The procedure begins with estimates of the Morisita Index, which is suitable for preliminary assessment…

Replaced article(s) found for q-fin.ST. https://arxiv.org/list/q-fin.ST/new
[1/1]:
- Reproducing the first and second moment of empirical degree distributions
Mattia Marzi, Francesca Giuffrida, Diego Garlaschelli, Tiziano Squartini

Characterisation of commercial SiC MOSFETs at deep-cryogenic temperatures
Megan Powell, Euan Parry, Conor McGeough, Alexander Zotov, Alessandro Rossi
https://arxiv.org/abs/2507.23109

Characterisation of commercial SiC MOSFETs at deep-cryogenic temperatures
Silicon carbide is a wide-bandgap semiconductor with an emerging CMOS technology platform and it is widely deployed in high power and harsh environment electronics. This material is also attracting interest for quantum technologies through its crystal defects, which can act as spin-based qubits or single-photon sources. In this work, we assess the cryogenic performance of commercial power MOSFETs to evaluate their suitability for CMOS-compatible quantum electronics. We perform a statistical stu…

Replaced article(s) found for cond-mat.dis-nn. https://arxiv.org/list/cond-mat.dis-nn/new
[1/1]:
- Statistical Mechanics of Support Vector Regression
Abdulkadir Canatar, SueYeon Chung

CUHK-EE Systems for the vTAD Challenge at NCMMSC 2025
Aemon Yat Fei Chiu, Jingyu Li, Yusheng Tian, Guangyan Zhang, Tan Lee
https://arxiv.org/abs/2507.23266 https://

CUHK-EE Systems for the vTAD Challenge at NCMMSC 2025
This paper presents the Voice Timbre Attribute Detection (vTAD) systems developed by the Digital Signal Processing & Speech Technology Laboratory (DSP&STL) of the Department of Electronic Engineering (EE) at The Chinese University of Hong Kong (CUHK) for the 20th National Conference on Human-Computer Speech Communication (NCMMSC 2025) vTAD Challenge. The proposed systems leverage WavLM-Large embeddings with attentive statistical pooling to extract robust speaker representations, followed by two…

A Statistical Approach to Quantifying Uncertainty in Meteoroid Physical Properties
Maximilian Vovk, Denis Vida, Peter G. Brown
https://arxiv.org/abs/2506.21701

A Statistical Approach to Quantifying Uncertainty in Meteoroid Physical Properties
Meteoroid bulk density is a critical value required for assessing impact risks to spacecraft, informing shielding and mission design. Direct bulk density measurements for sub-millimeter to millimeter-sized meteoroids are difficult, often relying on forward modeling without robust uncertainty estimates. Methods based solely on select observables can overlook noise-induced biases and non-linear relations between physical parameters. This study aims to automate the inversion of meteoroid physical …

from my link log —
Statistical shortcomings in standard math libraries, and how to fix them.
https://www.evanmiller.org/statistical-shortcomings-in-standard-math-libraries.html
saved 2024-05-28

Avalanching together: A model for sympathetic flaring
Louis-Simon Guit\'e, Paul Charbonneau, Antoine Strugarek
https://arxiv.org/abs/2506.23889 https:/…

Avalanching together: A model for sympathetic flaring
Avalanche models running in a self-organized critical regime have proven powerful in reproducing the power-law distributions and scale invariance that characterize the statistical properties of solar flares. They are often interpreted as representing an individual active region of the Sun. As a result, this class of models has rarely been applied to describe sympathetic flares $\unicode{x2014}$ solar eruptions that occur in close spatial and temporal proximity, seemingly driven by their mutual …

Deep learning-based segmentation of T1 and T2 cardiac MRI maps for automated disease detection
Andreea Bianca Popescu, Andreas Seitz, Heiko Mahrholdt, Jens Wetzl, Athira Jacob, Lucian Mihai Itu, Constantin Suciu, Teodora Chitiboi
https://arxiv.org/abs/2507.00903

Deep learning-based segmentation of T1 and T2 cardiac MRI maps for automated disease detection
Objectives Parametric tissue mapping enables quantitative cardiac tissue characterization but is limited by inter-observer variability during manual delineation. Traditional approaches relying on average relaxation values and single cutoffs may oversimplify myocardial complexity. This study evaluates whether deep learning (DL) can achieve segmentation accuracy comparable to inter-observer variability, explores the utility of statistical features beyond mean T1/T2 values, and assesses whether ma…

Distinguishing Quantum Software Bugs from Hardware Noise: A Statistical Approach
Ahmik Virani, Devraj, Anirudh Suresh, Lei Zhang, M V Panduranga Rao
https://arxiv.org/abs/2507.20475

Distinguishing Quantum Software Bugs from Hardware Noise: A Statistical Approach
Quantum computing in the Noisy Intermediate-Scale Quantum (NISQ) era presents significant challenges in differentiating quantum software bugs from hardware noise. Traditional debugging techniques from classical software engineering cannot directly resolve this issue due to the inherently stochastic nature of quantum computation mixed with noises from NISQ computers. To address this gap, we propose a statistical approach leveraging probabilistic metrics to differentiate between quantum software …

Enhanced Thermodynamic Framework for Third-Order Galaxy Correlation Functions: From Theory to Observations
Sameer Choudhary, Naseer Iqbal
https://arxiv.org/abs/2506.23829

Enhanced Thermodynamic Framework for Third-Order Galaxy Correlation Functions: From Theory to Observations
We present a comprehensive thermodynamic derivation and observational validation of the third-order galaxy correlation function, extending the seminal work of Iqbal et al [ 1, 2]. Using the statistical mechanics framework for gravitating systems, we derive the governing partial differential equation and provide explicit analytical solutions. Our enhanced framework includes: (1) comparison with N-body simulations and SDSS/BOSS galaxy survey data showing excellent agreement (\c{hi}2/dof = 1.27), …

Replaced article(s) found for cond-mat.stat-mech. https://arxiv.org/list/cond-mat.stat-mech/new
[2/2]:
- Symmetry Sectors in Chord Space and Relational Holography in the DSSYK
Sergio E. Aguilar-Gutierrez

Crosslisted article(s) found for quant-ph. https://arxiv.org/list/quant-ph/new
[1/1]:
- Quantum-inspired probability metrics define a complete, universal space for statistical learning
Logan S. McCarty

Scaled Beta Models and Feature Dilution for Dynamic Ticket Pricing
Jonathan R. Landers
https://arxiv.org/abs/2507.23767 https://arxiv.org/pdf/2507.23767

Scaled Beta Models and Feature Dilution for Dynamic Ticket Pricing
A novel approach is presented for identifying distinct signatures of performing acts in the secondary ticket resale market by analyzing dynamic pricing distributions. Using a newly curated, time series dataset from the SeatGeek API, we model ticket pricing distributions as scaled Beta distributions. This enables accurate parameter estimation from incomplete statistical data using a hybrid of quantile matching and the method of moments. Incorporating the estimated $α$ and $β$ parameters into R…

Physics-Aware Style Transfer for Adaptive Holographic Reconstruction
Chanseok Lee, Fakhriyya Mammadova, Jiseong Barg, Mooseok Jang
https://arxiv.org/abs/2507.00482

Physics-Aware Style Transfer for Adaptive Holographic Reconstruction
Inline holographic imaging presents an ill-posed inverse problem of reconstructing objects' complex amplitude from recorded diffraction patterns. Although recent deep learning approaches have shown promise over classical phase retrieval algorithms, they often require high-quality ground truth datasets of complex amplitude maps to achieve a statistical inverse mapping operation between the two domains. Here, we present a physics-aware style transfer approach that interprets the object-to-sensor …

Information geometry of L\'evy processes and financial models
Jaehyung Choi
https://arxiv.org/abs/2507.23646 https://arxiv.org/pdf/2507.23646

Information geometry of Lévy processes and financial models
We explore the information geometry of Lévy processes. As a starting point, we derive the $α$-divergence between two Lévy processes. Subsequently, the Fisher information matrix and the $α$-connection associated with the geometry of Lévy processes are computed from the $α$-divergence. In addition, we discuss statistical applications of this information geometry. As illustrative examples, we investigate the differential-geometric structures of various Lévy processes relevant to financial m…

Replaced article(s) found for q-fin.ST. https://arxiv.org/list/q-fin.ST/new
[1/1]:
- The Efficient Tail Hypothesis: An Extreme Value Perspective on Market Efficiency
Junshu Jiang, Jordan Richards, Rapha\"el Huser, David Bolin

Bayesian analysis of the causal reference-based model for missing data in clinical trials
Brendah Nansereko, Marcel Wolbers, James Carpenter
https://arxiv.org/abs/2507.00680

Bayesian analysis of the causal reference-based model for missing data in clinical trials
The statistical analysis of clinical trials is often complicated by missing data. Patients sometimes experience intercurrent events (ICEs), which usually (although not always) lead to missing subsequent outcome measurements for such individuals. The reference-based imputation methods were proposed by Carpenter et al. (2013) and have been commonly adopted for handling missing data due to ICEs when estimating treatment policy strategy estimands. Conventionally, the variance for reference-based es…

Low-Complexity 6DMA Rotation and Position Optimization Based on Statistical Channel Information
Qijun Jiang, Xiaodan Shao, Rui Zhang
https://arxiv.org/abs/2507.19309 https://

Low-Complexity 6DMA Rotation and Position Optimization Based on Statistical Channel Information
The six-dimensional movable antenna (6DMA) is a promising technology to fully exploit spatial variation in wireless channels by allowing flexible adjustment of three-dimensional (3D) positions and rotations of antennas at the transceiver. In this paper, we consider a 6DMA-equipped base station (BS) and aim to maximize the average sum logarithmic rate of all users served by the BS by jointly designing 6DMA surface positions and rotations based on statistical channel information (SCI). Different …

New line-driven wind mass-loss rates for OB stars with metallicities down to $0.01\,Z_\odot$
Jiri Krticka, Jiri Kubat, Iva Krtickova
https://arxiv.org/abs/2508.21702 https://

New line-driven wind mass-loss rates for OB stars with metallicities down to $0.01\,Z_\odot$
We provide new line-driven wind models for OB stars with metallicities down to $0.01\,Z_\odot$. The models were calculated with our global wind code METUJE, which solves the hydrodynamical equations from nearly hydrostatic photosphere to supersonically expanding stellar wind together with the equations of statistical equilibrium and the radiative transfer equation. The models predict the basic wind parameters, namely, the wind mass-loss rates and terminal velocities just from the stellar parame…

Replaced article(s) found for cond-mat.stat-mech. https://arxiv.org/list/cond-mat.stat-mech/new
[1/2]:
- Scaling behaviors at quantum and classical first-order transitions
Andrea Pelissetto, Ettore Vicari

An explicit power series result for the two type ASEP
David W. Ash
https://arxiv.org/abs/2507.22296 https://arxiv.org/pdf/2507.22296

An explicit power series result for the two type ASEP
Research in combinatorics has often focused on the ASEP (asymmetric simple exclusion process). The ASEP is inspired by processes in statistical mechanics, and involves particles of various species moving around a lattice. The particles do not change species. In the present paper, based on earlier results of Mortimer and Prellberg and others, we obtain a new power series results for the two type ASEP.

Imprints of gravitational-wave polarizations on projected tidal tensor in three dimensions
Yusuke Mikura, Teppei Okumura, Misao Sasaki
https://arxiv.org/abs/2507.23302 https://

Imprints of gravitational-wave polarizations on projected tidal tensor in three dimensions
Gravitational waves (GWs) distort galaxy shapes through the tidal effect, offering a novel avenue to probe the nature of gravity. In this paper, we investigate how extra GW polarizations beyond those predicted by general relativity imprint observable signatures on galaxy shapes. Since galaxy shapes are measured as two-dimensional images projected onto the celestial sphere, we present three-dimensional statistical quantities of the projected tidal tensor sourced by the tensor perturbation. We sh…

Tidal Inflation is Stronger for Misaligned Neptune-Sized Planets Than Aligned Ones
Ritika Sethi, Sarah Millholland
https://arxiv.org/abs/2506.24100 https:/…

Tidal Inflation is Stronger for Misaligned Neptune-Sized Planets Than Aligned Ones
Recent observations have revealed an intriguing abundance of polar-orbiting Neptune-sized planets, many of which exhibit unusually inflated radii. While such misaligned orbits point to a complex dynamical history, the connection between their orbital orientations and planetary structures remains poorly understood. In this study, we analyze a sample of 12 misaligned and 12 aligned planets using structure models that incorporate tidal heating. We use various statistical tests to demonstrate with …

Replaced article(s) found for q-fin.ST. https://arxiv.org/list/q-fin.ST/new
[1/1]:
- Wealth Thermalization Hypothesis
Klaus M. Frahm, Dima L. Shepelyansky

Statistical Inference for Differentially Private Stochastic Gradient Descent
Xintao Xia, Linjun Zhang, Zhanrui Cai
https://arxiv.org/abs/2507.20560 https://

Statistical Inference for Differentially Private Stochastic Gradient Descent
Privacy preservation in machine learning, particularly through Differentially Private Stochastic Gradient Descent (DP-SGD), is critical for sensitive data analysis. However, existing statistical inference methods for SGD predominantly focus on cyclic subsampling, while DP-SGD requires randomized subsampling. This paper first bridges this gap by establishing the asymptotic properties of SGD under the randomized rule and extending these results to DP-SGD. For the output of DP-SGD, we show that th…

Replaced article(s) found for cond-mat.stat-mech. https://arxiv.org/list/cond-mat.stat-mech/new
[1/1]:
- Engineering active motion in quantum matter
Alexander P. Antonov, Yuanjian Zheng, Benno Liebchen, Hartmut L\"owen

New Tests of Randomness for Circular Data
Shriya Gehlot, Arnab Kumar Laha
https://arxiv.org/abs/2506.23522 https://arxiv.org/pdf/2506…

New Tests of Randomness for Circular Data
Randomness or mutual independence is an important underlying assumption for most widely used statistical methods for circular data. Verifying this assumption is essential to ensure the validity and reliability of the resulting inferences. In this paper, we introduce two tests for assessing the randomness assumption in circular statistics, based on random circular arc graphs (RCAGs). We define and analyze RCAGs in detail, showing that their key properties depend solely on the i.i.d. nature of th…

MASCA: LLM based-Multi Agents System for Credit Assessment
Gautam Jajoo, Pranjal A Chitale, Saksham Agarwal
https://arxiv.org/abs/2507.22758 https://arxiv.…

MASCA: LLM based-Multi Agents System for Credit Assessment
Recent advancements in financial problem-solving have leveraged LLMs and agent-based systems, with a primary focus on trading and financial modeling. However, credit assessment remains an underexplored challenge, traditionally dependent on rule-based methods and statistical models. In this paper, we introduce MASCA, an LLM-driven multi-agent system designed to enhance credit evaluation by mirroring real-world decision-making processes. The framework employs a layered architecture where speciali…

Replaced article(s) found for cond-mat.stat-mech. https://arxiv.org/list/cond-mat.stat-mech/new
[1/1]:
- Fast Sampling of Protein Conformational Dynamics
Michael A. Sauer, Souvik Mondal, Brandon Neff, Sthitadhi Maiti, Matthias Heyden

Bag of Coins: A Statistical Probe into Neural Confidence Structures
Agnideep Aich, Ashit Baran Aich, Md Monzur Murshed, Sameera Hewage, Bruce Wade
https://arxiv.org/abs/2507.19774

Bag of Coins: A Statistical Probe into Neural Confidence Structures
Modern neural networks, despite their high accuracy, often produce poorly calibrated confidence scores, limiting their reliability in high-stakes applications. Existing calibration methods typically post-process model outputs without interrogating the internal consistency of the predictions themselves. In this work, we introduce a novel, non-parametric statistical probe, the Bag-of-Coins (BoC) test, that examines the internal consistency of a classifier's logits. The BoC test reframes confidenc…

Palm distributions of superposed point processes for statistical inference
Mario Beraha, Federico Camerlenghi, Lorenzo Ghilotti
https://arxiv.org/abs/2508.20924 https://

Palm distributions of superposed point processes for statistical inference
Palm distributions play a central role in the study of point processes and their associated summary statistics. In this paper, we characterize the Palm distributions of the superposition of two independent point processes, establishing a simple mixture representation depending on the point processes' Palm distributions and moment measures. We explore two statistical applications enabled by our main result. First, we consider minimum contrast estimation for superposed point processes based on Ri…

A Privacy-Preserving Data Collection Method for Diversified Statistical Analysis
Hao Jiang, Quan Zhou, Dongdong Zhao, Shangshang Yang, Wenjian Luo, Xingyi Zhang
https://arxiv.org/abs/2507.17180

A Privacy-Preserving Data Collection Method for Diversified Statistical Analysis
Data perturbation-based privacy-preserving methods have been widely adopted in various scenarios due to their efficiency and the elimination of the need for a trusted third party. However, these methods primarily focus on individual statistical indicators, neglecting the overall quality of the collected data from a distributional perspective. Consequently, they often fall short of meeting the diverse statistical analysis requirements encountered in practical data analysis. As a promising sensit…

Detection of collective and point anomalies at the presence of trend and seasonality
Yiyin Zhang, Florian Pein, Idris Eckley
https://arxiv.org/abs/2508.21128 https://

Detection of collective and point anomalies at the presence of trend and seasonality
Detecting anomalies in time series data is a challenging task with broad relevance in many applications. Existing methods work effectively only under idealized conditions, typically focusing on point anomalies or assuming a constant baseline. Our approach overcomes these limitations by detecting both collective and point anomalies, while allowing for polynomial trends and seasonal patterns. We establish statistical theory demonstrating that our method accurately decomposes the time series into …

[2025-09-01 Mon (UTC), no new articles found for q-fin.ST Statistical Finance]
toXiv_bot_toot

Some Results on Point Estimation of the Association Parameter of a Bivariate Frank Copula
Yen-Anh Thi Pham, Huynh To Uyen, Nabendu Pal
https://arxiv.org/abs/2506.23110

Some Results on Point Estimation of the Association Parameter of a Bivariate Frank Copula
This work deals with estimation of the association parameter of a bivariate Frank Copula in a comprehensive way. Even though Frank Copula is a member of Archimedean class of copulas, and has been widely used in finance, relatively little attention has been paid to its association parameter from a statistical inferential point of view. Most of the existing works which have used Frank Copula have focused on estimating the parameter computationally, and then proceeded with its application in the a…

[2025-09-01 Mon (UTC), 3 new articles found for cond-mat.stat-mech Statistical Mechanics]
toXiv_bot_toot

Asteroid Rotation Periods: Statistical Analysis in the Diameter-Spin Distribution
Maryam Nastaran, Atila Poro, Raziyeh Hosseini, Matin Najarzadeh
https://arxiv.org/abs/2508.17415

Asteroid Rotation Periods: Statistical Analysis in the Diameter-Spin Distribution
This study examines the rotational characteristics of asteroids through statistical modeling of the diameter-period relationship. A statistical evaluation of the diameter-period relationship was conducted using a dataset of 34,326 asteroids. Clustering identified three main groups, including a dense cluster below the spin barrier, a population of small, fast-rotating asteroids, and a more diffuse group. Geometric and density-based analyses showed that the densest region consists of objects with…

[2025-08-01 Fri (UTC), 1 new article found for q-fin.ST Statistical Finance]
toXiv_bot_toot

Goodness-of-fit Tests for Combined Unilateral and Bilateral Data
Jia Zhou, Chang-Xing Ma
https://arxiv.org/abs/2506.22605 https://arx…

Goodness-of-fit Tests for Combined Unilateral and Bilateral Data
Clinical trials involving paired organs often yield a mixture of unilateral and bilateral data, where each subject may contribute either one or two responses under certain circumstances. While unilateral responses from different individuals can be treated as independent, bilateral responses from the same individual are likely correlated. Various statistical methods have been developed to account for this intra-subject correlation in the bilateral data, and in practice it is crucial to select an…

[2025-09-02 Tue (UTC), no new articles found for cond-mat.stat-mech Statistical Mechanics]
toXiv_bot_toot

Predicting Parkinson's Disease Progression Using Statistical and Neural Mixed Effects Models: A Comparative Study on Longitudinal Biomarkers
Ran Tong, Lanruo Wang, Tong Wang, Wei Yan
https://arxiv.org/abs/2507.20058

Predicting Parkinson's Disease Progression Using Statistical and Neural Mixed Effects Models: A Comparative Study on Longitudinal Biomarkers
Predicting Parkinson's Disease (PD) progression is crucial, and voice biomarkers offer a non-invasive method for tracking symptom severity (UPDRS scores) through telemonitoring. Analyzing this longitudinal data is challenging due to within-subject correlations and complex, nonlinear patient-specific progression patterns. This study benchmarks LMMs against two advanced hybrid approaches: the Generalized Neural Network Mixed Model (GNMM) (Mandel 2021), which embeds a neural network within a GLMM …

Developing a Synthetic Socio-Economic Index through Autoencoders: Evidence from Florence's Suburban Areas
Giulio Grossi, Emilia Rocco
https://arxiv.org/abs/2506.23849

Developing a Synthetic Socio-Economic Index through Autoencoders: Evidence from Florence's Suburban Areas
The interest in summarizing complex and multidimensional phenomena often related to one or more specific sectors (social, economic, environmental, political, etc.) to make them easily understandable even to non-experts is far from waning. A widely adopted approach for this purpose is the use of composite indices, statistical measures that aggregate multiple indicators into a single comprehensive measure. In this paper, we present a novel methodology called AutoSynth, designed to condense potent…

[2025-07-02 Wed (UTC), 1 new article found for q-fin.ST Statistical Finance]
toXiv_bot_toot

[2025-07-01 Tue (UTC), 8 new articles found for cond-mat.stat-mech Statistical Mechanics]
toXiv_bot_toot

CoMMiT: Co-informed inference of microbiome-metabolome interactions via transfer learning
Leiyue Li, Chenglong Ye, Tim Randolph, Meredith Hullar, Johanna Lampe, Marian Neuhouser, Daniel Raftery, Yue Wang
https://arxiv.org/abs/2506.24013

CoMMiT: Co-informed inference of microbiome-metabolome interactions via transfer learning
Recent multi-omic microbiome studies enable integrative analysis of microbes and metabolites, uncovering their associations with various host conditions. Such analyses require multivariate models capable of accounting for the complex correlation structures between microbes and metabolites. However, existing multivariate models often suffer from low statistical power for detecting microbiome-metabolome interactions due to small sample sizes and weak biological signals. To address these challenge…

[2025-07-01 Tue (UTC), 1 new article found for q-fin.ST Statistical Finance]
toXiv_bot_toot

[2025-07-02 Wed (UTC), 4 new articles found for cond-mat.stat-mech Statistical Mechanics]
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[2025-08-01 Fri (UTC), 3 new articles found for cond-mat.stat-mech Statistical Mechanics]
toXiv_bot_toot

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Statistical Inference of the Value Function for Reinforcement Learning in Infinite Horizon Settings
C. Shi, S. Zhang, W. Lu, R. Song

When Is Causal Inference Possible? A Statistical Test for Unmeasured Confounding
Muye Liu, Jun Xie
https://arxiv.org/abs/2508.20366 https://arxiv.org/pdf/2…

When Is Causal Inference Possible? A Statistical Test for Unmeasured Confounding
This paper clarifies a fundamental difference between causal inference and traditional statistical inference by formalizing a mathematical distinction between their respective parameters. We connect two major approaches to causal inference, the potential outcomes framework and causal structure graphs, which are typically studied separately. While the unconfoundedness assumption in the potential outcomes framework cannot be assessed from an observational dataset alone, causal structure graphs he…

Approximate calculation of multidimensional first passage times
James F. Lutsko
https://arxiv.org/abs/2508.21607 https://arxiv.org/pdf/2508.21607

Approximate calculation of multidimensional first passage times
The general, multidimensional barrier crossing problem for diffusive processes under the action of conservative forces is studied with the goal of developing tractable approximations. Particular attention is given to the effect of different statistical interpretations of the stochastic differential equation and to the relation between the approximations and the known, exact solutions to the one-dimensional problem. Beginning with a reasonable, but heuristic, simplifying assumption, a one-dimens…

Scaling, Fractal Dynamics and Critical Exponents{: Application} in a non-integer dimensional Ising model
Henrique A. de Lima, Ismael S. S. Carrasco, Marcio Santos, Fernando A. Oliveira
https://arxiv.org/abs/2507.00956

Scaling, Fractal Dynamics and Critical Exponents{: Application} in a non-integer dimensional Ising model
Moving beyond simple associations, researchers need tools to quantify how variables influence each other in space and time. Correlation functions provide a mathematical framework for characterizing these essential dependencies, revealing insights into causality, structure, and hidden patterns within complex systems. In physical systems with many degrees of freedom, such as gases, liquids, and solids, a statistical analysis of these correlations is essential. For a field $Ψ(\vec{x},t)$ that dep…

Replaced article(s) found for q-fin.ST. https://arxiv.org/list/q-fin.ST/new
[1/1]:
- Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literatu...
Yisong Chen, Chuqing Zhao, Yixin Xu, Chuanhao Nie, Yixin Zhang

Is External Information Useful for Data Fusion? An Evaluation before Acquisition
Guorong Dai, Lingxuan Shao, Jinbo Chen
https://arxiv.org/abs/2507.22351 https://

Is External Information Useful for Data Fusion? An Evaluation before Acquisition
We consider a general statistical estimation problem involving a finite-dimensional target parameter vector. Beyond an internal data set drawn from the population distribution, external information, such as additional individual data or summary statistics, can potentially improve the estimation when incorporated via appropriate data fusion techniques. However, since acquiring external information often incurs costs, it is desirable to assess its utility beforehand using only the internal data. …

Replaced article(s) found for cond-mat.stat-mech. https://arxiv.org/list/cond-mat.stat-mech/new
[1/1]:
- Thermodynamic Circuits: Association of thermoelectric converters in stationary non-equilibrium
Paul Raux, Christophe Goupil, Gatien Verley

Replaced article(s) found for cond-mat.stat-mech. https://arxiv.org/list/cond-mat.stat-mech/new
[1/1]:
- Boundary conditions dependence of the phase transition in the quantum Newman-Moore model
Konstantinos Sfairopoulos, Luke Causer, Jamie F. Mair, Juan P. Garrahan