Clay weekly context brief for the Statistics category (ISO week 2026-W32).

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. Foundation-Model Earth Representations Enable Regional-Scale Forest Aboveground Biomass Monitoring Across the Northeastern United States
   Source: stat.AP (Applications)
   Link: https://arxiv.org/abs/2607.27217
   Forest aboveground biomass (AGB) is a critical indicator of ecosystem productivity and terrestrial carbon storage, yet regional carbon monitoring remains constrained by the sparse spatial and temporal availability of field inventories and airborne structural measurements.
2. Sinkhorn Hamiltonian Monte Carlo for Entropic Optimal Transport Generalized Bayes
   Source: stat.CO (Computation)
   Link: https://arxiv.org/abs/2607.28015
   Bayesian posterior sampling is a ubiquitous paradigm for problems where a point estimate of parameters is not sufficient, such as risk analysis and uncertainty quantification.
3. A New Measure of Dependence Between Continuous and Multinomial Random Variables
   Source: stat.ME (Methodology)
   Link: https://arxiv.org/abs/2607.27381
   A novel measure of dependence between a continuous random variable and a multinomial random variable is introduced.
4. An analysis of binary isotonic regression: degrees of freedom and implications for calibration
   Source: stat.ML (Machine Learning)
   Link: https://arxiv.org/abs/2607.27301
   Isotonic regression is a canonical tool for estimating monotone functions and calibrating probabilistic predictors.
5. Bringing Closure to False Discovery Rate Control: A General Principle for Multiple Testing
   Source: stat.TH (Statistics Theory)
   Link: https://arxiv.org/abs/2509.02517
   We present a novel necessary and sufficient principle for multiple testing methods controlling an expected loss.
6. The Rise of Null Hypothesis Significance Testing (NHST): Institutional Massification and the Emergence of a Procedural Epistemology
   Source: stat.OT (Other Statistics)
   Link: https://arxiv.org/abs/2603.14757
   It has long been a puzzle why, despite sustained reform efforts, many applied scientific fields remain dominated by Null Hypothesis Significance Testing (NHST), a framework that dichotomizes study results and privileges "statistically significant" findings.
7. Psych-ECA: A Reproducible Semi-Synthetic Benchmark for Synthetic Control Arms in Longitudinal Psychiatry
   Source: stat.AP (Applications)
   Link: https://arxiv.org/abs/2607.27224
   External and synthetic control arms (ECAs) are entering psychiatric drug development, but the field lacks a benchmark that evaluates the properties regulators care about: not only how accurately a method reconstructs untreated trajectories, but whether its uncertainty is calibrated, whether it is robust to the informative observation times common in mental-health records (sicker patients are seen more often), and what false-positive rate it induces in go/no-go trial decisions.
8. Reduce-Rank Matrix Integer-Valued Autoregressive Model
   Source: stat.CO (Computation)
   Link: https://arxiv.org/abs/2509.03338
   Integer-valued time series are widely present in many fields, such as finance, economics, disease transmission, and traffic flow.
9. Adaptive Nystr\"om for Gaussian Process Regression
   Source: stat.ME (Methodology)
   Link: https://arxiv.org/abs/2607.27427
   Gaussian Process Regression (GPR) is a robust framework for uncertainty quantification, yet its $O(n^3)$ complexity limits its scalability.
10. HOMER: Huber-of-Means for Efficient and Robust Estimation in Hilbert Spaces
   Source: stat.ML (Machine Learning)
   Link: https://arxiv.org/abs/2607.27532
   Heavy tails weaken high-confidence control for the empirical mean.
11. Scalable Estimation of Crossed Random Effects Models via Multi-way Discretization
   Source: stat.TH (Statistics Theory)
   Link: https://arxiv.org/abs/2507.15593
   Cross-classified data frequently arise in scientific fields such as education, healthcare, and social sciences.
12. Who Moved? Ecological Estimates of Turnout & Coalition Support by Ethnicity and Age in Johor, Malaysia, 2022-2026
   Source: stat.AP (Applications)
   Link: https://arxiv.org/abs/2607.27864
   At the July 2026 Johor state election, Barisan Nasional (BN) swept the state and nearly doubled its vote relative to 2022.
13. Additive Matrix Integer-Valued Autoregressive Model
   Source: stat.CO (Computation)
   Link: https://arxiv.org/abs/2605.30958
   Contemporary data-driven and technology-integrated era, various matrix-valued integer-valued time series, such as cross-regional crime statistics, multi-category sales records, and network traffic matrices, exhibit high dimensionality, complex structures, and strong row-column intertwined dependencies.
14. Co-clustering of Response and Covariate Variables by Tri-Factorizing Their Non-negative Regression Coefficient Matrix
   Source: stat.ME (Methodology)
   Link: https://arxiv.org/abs/2607.27474
   Two-block data---two sets of variables measured on the same individuals, such as microbial taxa and metabolites---raise the question of how \emph{groups} of covariate variables relate to \emph{groups} of response variables.
15. Robust Wavelength Selection for Partial Least Squares Sugar Content Estimation Using Combinatorial Bayesian Optimization
   Source: stat.ML (Machine Learning)
   Link: https://arxiv.org/abs/2607.27645
   Wavelength selection is one of the important preprocessing methods in near-infrared spectroscopy to improve prediction accuracy and interpretability of spectral data.
16. Learning to Detect Cyber Attacks: Neural Anomaly Detection for Cybersecurity with Theoretical Insights
   Source: stat.TH (Statistics Theory)
   Link: https://arxiv.org/abs/2409.08521
   In cybersecurity practice, new forms of cyberattacks continuously emerge, deliberately designed to evade defense systems that rely on previously observed behaviors.
17. What exam scores can and cannot prove about unauthorized AI assistance: Evidence from a highly public classroom episode
   Source: stat.AP (Applications)
   Link: https://arxiv.org/abs/2607.27978
   In spring 2026, an economics professor at Brown University gave a take-home midterm and, after unusually high scores, made the final exam proctored.
18. Handling Missingness and Censoring in Dirichlet Mixture Models
   Source: stat.CO (Computation)
   Link: https://arxiv.org/abs/2607.27403
   Incomplete compositional data analysis faces a fundamental limitation: likelihood-based methods for compositional models generally require fully observed compositions, making it difficult to accommodate missing or censored proportions directly on the simplex.
19. The Continuous Latent Ornstein-Uhlenbeck Dynamics Framework: A Scalable Latent Process Model for Multivariate Longitudinal Categorical Data
   Source: stat.ME (Methodology)
   Link: https://arxiv.org/abs/2607.27520
   Longitudinal biomedical studies increasingly collect irregularly sampled, multivariate categorical data that imperfectly reflect disease progression.
20. Robust Estimation of Sparse Numerical Vectors under Local Differential Privacy
   Source: stat.ML (Machine Learning)
   Link: https://arxiv.org/abs/2607.27815
   Local differential privacy (LDP) protocols are vulnerable to poisoning attacks.
21. Exact Tail Asymptotics of Dirichlet Distributions
   Source: stat.TH (Statistics Theory)
   Link: https://arxiv.org/abs/0904.0144
   Let $\X=A^\top R\U$ be a linearly transformed generalised symmetrised Dirichlet scale mixture in $\R^k$, $k\ge2$.
22. Bridging Probabilistic LLMs and Deterministic Statistical Validation: The PROVE Multi-Agent Framework for Clinical Trial Reporting
   Source: stat.AP (Applications)
   Link: https://arxiv.org/abs/2607.28218
   Ensuring the accuracy and consistency of clinical trial Tables, Figures, and Listings (TFLs) remains a major challenge in regulatory reporting.
23. Mesh Invariant Infinite Dimensional Adaptive MCMC for Latent Gaussian Processes
   Source: stat.CO (Computation)
   Link: https://arxiv.org/abs/1804.04859
   We introduce mesh-invariant adaptive Markov chain Monte Carlo methods for Gaussian-process posteriors arising in infinite-dimensional Bayesian inference.
24. Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches
   Source: stat.ME (Methodology)
   Link: https://arxiv.org/abs/2607.27542
   While randomization justifies the use of unadjusted effect estimators in randomized trials, there is growing interest in covariate adjustment to improve precision.
25. Generalization and Trade-off in Adversarial Training: An RKHS Perspective via Kernel Integral Operators
   Source: stat.ML (Machine Learning)
   Link: https://arxiv.org/abs/2607.27995
   Adversarial training has emerged as a powerful approach for protecting models against adversarial attacks in a broad range of real-world applications.
26. The Kaplan-Meier estimator as a limit function of Efron's self-consistency iterations
   Source: stat.TH (Statistics Theory)
   Link: https://arxiv.org/abs/2607.27068
   In 1967 Efron showed that the Kaplan-Meier estimator can be defined as a fixed point of a certain mapping, calling this property ``self-consistency''.
27. Snapshot plots: displaying summary tables as parallel univariate plots with consistent color highlighting
   Source: stat.AP (Applications)
   Link: https://arxiv.org/abs/2607.28302
   For empirical studies, social and health scientists give background characteristics of their sample and summarize them in the famous "Table 1".
28. Local Quasi-Exponential Growth Models: Kernel Differential Equation Regression and Mouse Tumor Growth Data
   Source: stat.CO (Computation)
   Link: https://arxiv.org/abs/2505.00231
   Local polynomial regression faces several challenges when dealing with sparse data.
29. Asymptotic emergence of statistically supported false causal interpretation under unmeasured confounding
   Source: stat.ME (Methodology)
   Link: https://arxiv.org/abs/2607.27593
   Unmeasured confounding is widely recognized as a limitation of observational causal inference, but its implications for data-driven causal discovery are often understated.
30. On a joint simultaneous learning of relevant feature subsets and subspaces in regression-like problems
   Source: stat.ML (Machine Learning)
   Link: https://arxiv.org/abs/2607.28080
   We extend a recently introduced Entropy-Optimal Manifold Clustering (EOMC) to allow for a joint simultaneous identification of subsets and subspaces of relevant features in nonstationary and nonlinear regression problems.
31. The Phase Transition in Online PCA Depends on $n/d\log(d)$, not $n/d$
   Source: stat.TH (Statistics Theory)
   Link: https://arxiv.org/abs/2607.23914
   High dimensional statistical theory has established the importance of constant aspect ratio, when the number of dimensions ($d$) and samples ($n$) satisfy $n,d\to\infty$ with $n/d\to \gamma\in(0,\infty)$, in understanding the limits of canonical estimation problems.

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 416 available items for this weekly brief.