Clay weekly context brief for the Statistics category (ISO week 2026-W35).

Clay tracks publications from the Statistics feed list. Below are recent items from this category, each with its source and a short description of what the publication covers when one is available in the source feed.

Recent publications:
1. A Bayesian Time-Varying SEIARD Model for State-Level COVID-19 Transmission and Mortality in the United States
   Source: stat.AP (Applications)
   Link: https://arxiv.org/abs/2608.19716
   We conduct a retrospective analysis of COVID-19 transmission dynamics across U.S.
2. skchange: Fast and Flexible Algorithms for Changepoint Detection
   Source: stat.CO (Computation)
   Link: https://arxiv.org/abs/2608.19767
   Skchange is an open-source Python library for detecting structural changes in time series.
3. Discrete Diffusion Inference-Time Control with Nested Sequential Monte Carlo
   Source: stat.ML (Machine Learning)
   Link: https://arxiv.org/abs/2608.20123
   We study inference-time control for text generation in discrete diffusion language models, where the goal is to steer sampling toward sequence-level rewards without retraining.
4. Fast Near-Optimal Estimation over Symmetric Norm Balls
   Source: stat.TH (Statistics Theory)
   Link: https://arxiv.org/abs/2606.01554
   This short note proposes a polynomial-time algorithm for near-optimal Euclidean estimation of a signal constrained to lie in the unit ball of a symmetric norm, where the symmetry is with respect to a known basis and the norm is accessible through an evaluation oracle.
5. Evaluating Spacing Tests of Multi-Modality
   Source: stat.ME (Methodology)
   Link: https://arxiv.org/abs/2608.18243
   The spacing of data contains information about the underlying modality.
6. LLteacher: A Tool for the Integration of Generative AI into Statistics Assignments
   Source: stat.OT (Other Statistics)
   Link: https://arxiv.org/abs/2512.23053
   As generative AI becomes increasingly embedded in everyday life, the thoughtful and intentional integration of AI-based tools into statistics education has become essential.
7. Copula-Based Reconstruction and Clustering of Coccidioides Minimum Inhibitory Concentration Profiles
   Source: stat.AP (Applications)
   Link: https://arxiv.org/abs/2608.19722
   Coccidioidomycosis is a fungal lung infection endemic to parts of the Pacific Northwest and southwestern United States, Mexico, Central America, and South America.
8. Efficient Poisson Subsampling for the Partially Linear Additive Cox Model
   Source: stat.CO (Computation)
   Link: https://arxiv.org/abs/2608.19599
   To address the computational and storage challenges often encountered in large-scale survival data analysis, we propose an efficient Poisson subsampling method for the partially linear additive Cox model.
9. Transfer Learning in Nonparametric Regression with Deep ReLU Networks
   Source: stat.ML (Machine Learning)
   Link: https://arxiv.org/abs/2608.20255
   This paper develops a general transfer learning framework for nonparametric regression with data consisting of multiple groups.
10. Semiparametric Efficiency of Residual Correlation Testing under Gaussian Additive Noise Models
   Source: stat.TH (Statistics Theory)
   Link: https://arxiv.org/abs/2606.01011
   This paper studies conditional independence testing under the Gaussian additive noise model (GANM), where two variables are modeled as nonlinear functions of covariates with independent bivariate Gaussian regression errors.
11. Debiased Inference for AI-Generated Data without Gold-Standard Labels: Identification via Multiple Imperfect Measurements
   Source: stat.ME (Methodology)
   Link: https://arxiv.org/abs/2608.18294
   An increasing number of scholars use AI to measure variables they subsequently include in downstream analyses.
12. Integrating Temporal Disaggregation and Distributed Lag Nonlinear Models for Bayesian Spatio-Temporal Disease Mapping with High-Resolution Environmental
   Source: stat.AP (Applications)
   Link: https://arxiv.org/abs/2608.20046
   Environmental conditions are major drivers of malaria transmission, but epidemiological analyses are often constrained by temporal misalignment between health outcomes reported at coarse time scales and environmental exposures available at finer resolutions.
13. A Bayesian Edge-Space Framework for Whole-Connectome Inference in Multisite Autism Neuroimaging
   Source: stat.CO (Computation)
   Link: https://arxiv.org/abs/2608.20243
   Autism spectrum disorder (ASD) is associated with heterogeneous alterations across distributed brain systems, creating challenges for whole-connectome inference.
14. Improved Confidence Estimates for Black-Box Large Language Models
   Source: stat.ML (Machine Learning)
   Link: https://arxiv.org/abs/2608.19323
   Uncertainty quantification (UQ) is essential for the safe deployment of large language models (LLMs).
15. Stein's method for the matrix normal distribution
   Source: stat.TH (Statistics Theory)
   Link: https://arxiv.org/abs/2601.11422
   This work presents the first systematic development of Stein's method for matrix distributions.
16. Causal Mediation Analysis for an Interrupted Time Series: Stabilized Mediator Weighting with an Application to a Vehicle Emissions Policy
   Source: stat.ME (Methodology)
   Link: https://arxiv.org/abs/2608.18326
   Population-level policies are introduced at a fixed time and evaluated from a single series of aggregate outcomes, and the interrupted time series design estimates the total shift in an outcome after the intervention.
17. Causal Inference under Interference with Learned Exposure Mappings
   Source: stat.AP (Applications)
   Link: https://arxiv.org/abs/2608.19224
   Exposure mappings are often assumed to be known in causal spillover analyses.
18. SSLfmm: An R Package for Semi-Supervised Learning with Mixed Missingness
   Source: stat.CO (Computation)
   Link: https://arxiv.org/abs/2512.03322
   Partially labelled samples arise when features are observed for all data, but class labels are available for only a subset.
19. Causal Generalization of Continuous Treatment Effects under Covariate Shift
   Source: stat.ML (Machine Learning)
   Link: https://arxiv.org/abs/2608.19383
   Average dose-response functions are widely used to summarize causal effects of continuous treatments, but most existing methods assume that the observed sample represents the target population.
20. Stable Central Limit Theorems for Discrete-Time Lag Martingale Difference Arrays: Applications to Dynamic Causal Inference
   Source: stat.TH (Statistics Theory)
   Link: https://arxiv.org/abs/2510.06524
   Recent work in dynamic causal inference introduced a class of discrete-time stochastic processes that generalize martingale difference sequences and arrays as follows: the random variates in each sequence have expectation zero given certain lagged filtrations but not given the natural filtration.
21. Centroid-Referenced Mahalanobis Matching (CRM): A Scalable, Representation-Based Framework for Causal Inference in Large Observational Studies
   Source: stat.ME (Methodology)
   Link: https://arxiv.org/abs/2608.18417
   Matching for causal inference can be computationally expensive at scale and can silently change the target population when overlap is limited.
22. Estimating Negative Income Distributions via Data Fusion with Vine Copula-based Imputation
   Source: stat.AP (Applications)
   Link: https://arxiv.org/abs/2608.19585
   Imputation-based data fusion combines datasets by imputing missing variables in one dataset using information from the other.
23. Structured Secant Methods to Select Smoothing Parameters for General Smooth Models
   Source: stat.CO (Computation)
   Link: https://arxiv.org/abs/2606.26804
   General smooth models replace parameters of a regular likelihood with additive models.
24. DeltaMomentum: A Key-Value based Anisotropic Momentum Update via Delta Rule
   Source: stat.ML (Machine Learning)
   Link: https://arxiv.org/abs/2608.19491
   Most modern optimizers form their momentum as an exponential moving average (EMA) of past gradients, forgetting every direction at one fixed rate.
25. Existence of penalised likelihood estimates and posterior propriety of separable prior distributions for Gaussian precision matrices
   Source: stat.TH (Statistics Theory)
   Link: https://arxiv.org/abs/2505.20005
   Penalised likelihoods are often used for sparse estimation of a Gaussian precision matrix.
26. A seamless dose-optimization design for monotherapy and combination therapy
   Source: stat.ME (Methodology)
   Link: https://arxiv.org/abs/2608.18435
   The emergence of molecular-targeted agents and immune-oncology therapies has fundamentally transformed oncology drug development, necessitating evolution beyond traditional dose-finding approaches designed for cytotoxic agents.
27. Variational Goal-Oriented Optimal Experimental Design for Mixed-Distribution Quantities of Interest: Application to Ship Roll Safety
   Source: stat.AP (Applications)
   Link: https://arxiv.org/abs/2608.19631
   Goal-oriented optimal experimental design (GO-OED) selects experiments according to the expected information gain (EIG) about a quantity of interest (QoI) rather than the full parameter vector.
28. Windowed thinning and query complexity for the bouncy particle and Zigzag samplers
   Source: stat.CO (Computation)
   Link: https://arxiv.org/abs/2607.28413
   Let $\mu(d x)\propto e^{-U(x)} d x$ on $\R^d$, where $U$ is $m$-strongly convex and $L$-smooth, and denote by $\kappa=L/m$ the condition number.
29. A Causal Inference Approach for Evaluating Diagnostic Tests and AI-Enabled Medical Devices: From Effect Modification to Information-Augmented Decision-Making
   Source: stat.ML (Machine Learning)
   Link: https://arxiv.org/abs/2608.19501
   Diagnostic medical tests and devices provide useful information for evaluating the potential benefits and risks of therapeutic treatments.
30. The mutual arrangement of Wright-Fisher diffusion path measures and its impact on parameter estimation
   Source: stat.TH (Statistics Theory)
   Link: https://arxiv.org/abs/2410.15955
   The Wright-Fisher diffusion is a fundamentally important model of evolution encompassing genetic drift, mutation, and natural selection.
31. gridcp: Fast Online Changepoint Detection in Python
   Source: stat.ME (Methodology)
   Link: https://arxiv.org/abs/2608.18695
   Online changepoint detection is the problem of detecting distributional changes in a data stream in real-time.

Sources in this brief: stat.AP (Applications); stat.CO (Computation); stat.ME (Methodology); stat.ML (Machine Learning); stat.OT (Other Statistics); stat.TH (Statistics Theory).
Selected 31 of 447 available items for this weekly brief.