Clay weekly context brief for the Statistics category (ISO week 2026-W46).

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. Sparse Probabilistic Richardson Extrapolation
   Source: stat.CO (Computation)
   Link: https://arxiv.org/abs/2604.02072
   Almost every numerical task can be cast as extrapolation with respect to the fidelity or tolerance parameters of a consistent numerical method.
2. Anchors Away: Navigating Single-Arm Indirect Comparisons with Multilevel Unanchored Meta-Regression (ML-UMR-SA)
   Source: stat.AP (Applications)
   Link: https://arxiv.org/abs/2606.20341
   Unanchored indirect treatment comparisons using single-arm evidence are increasingly used in health technology assessment (HTA) when anchored comparisons of randomized evidence are not possible.
3. A High Dimensional Wild Bootstrap Max-Test for Detecting the Presence of Significant Predictors
   Source: stat.TH (Statistics Theory)
   Link: https://arxiv.org/abs/2604.27813
   We construct a block bootstrap max-test for detecting the presence of significant predictors in a high dimensional setting, allowing for weakly dependent and heterogeneous (possibly non-stationary) data.
4. Dynamic Bayesian Predictive Stacking via Markovian Spatiotemporal Propagation
   Source: stat.CO (Computation)
   Link: https://arxiv.org/abs/2602.08544
   This manuscript develops computationally efficient online learning for multivariate spatiotemporal models.
5. Sparse Network Inference under Imperfect Detection and its Application to Ecological Networks
   Source: stat.AP (Applications)
   Link: https://arxiv.org/abs/2604.18820
   Recovering latent structure from count data has received considerable attention in network inference, particularly when one seeks both cross-group interactions and within-group similarity patterns in bipartite networks, which are widely used in ecology research.
6. Phase transition for conditional covariance matrices estimated by importance sampling, and implications for cross-entropy schemes in high dimension
   Source: stat.TH (Statistics Theory)
   Link: https://arxiv.org/abs/2511.11351
   Motivated by recent high-dimensional reliability algorithms, we study a random matrix model corresponding to the estimation by importance sampling of $\Sigma_A$, the covariance matrix of a Gaussian vector conditioned by a rare event $A$.
7. FasterRW: An Efficient Prior-Assisted Random Walk Solver for Thermal Analysis
   Source: stat.CO (Computation)
   Link: https://arxiv.org/abs/2611.08490
   Thermal simulation is becoming increasingly critical in modern IC design and manufacturing.
8. Competing Risk Analysis in Cardiovascular Outcome Trials: A Simulation Comparison of Cox and Fine-Gray Models
   Source: stat.AP (Applications)
   Link: https://arxiv.org/abs/2602.16031
   Cardiovascular outcome trials commonly face competing risks when non-CV death prevents observation of major adverse cardiovascular events (MACE).
9. Functional Extreme-PLS
   Source: stat.TH (Statistics Theory)
   Link: https://arxiv.org/abs/2410.05517
   An extreme dimension reduction method is proposed, extending the Extreme-PLS approach to the discretized functional framework, where the covariate lies in the infinite-dimensional Hilbert space ($L^2([0,1])$) but is partially observed on a dense time grid.
10. A Generalized Bayesian Approach to Clustering with Dimension Reduction
   Source: stat.CO (Computation)
   Link: https://arxiv.org/abs/2611.09698
   Generalized Bayes product partition models provide a probabilistic counterpart to loss-based clustering.
11. Assessment of evidence against homogeneity in exhaustive subgroup treatment effect plots
   Source: stat.AP (Applications)
   Link: https://arxiv.org/abs/2602.06910
   Exhaustive subgroup treatment effect plots are constructed by displaying all subgroup treatment effects of interest against subgroup sample size, providing a useful overview of the observed treatment effect heterogeneity in a clinical trial.
12. Some ordering properties of highest and lowest order statistics with exponentiated Gumble type-II distributed components
   Source: stat.TH (Statistics Theory)
   Link: https://arxiv.org/abs/1904.08730
   This paper studies stochastic comparisons of the highest and lowest order statistics arising from independent Exponentiated Gumbel Type-II distributed random variables.
13. Locally Adaptive Conformal Prediction for Polynomial Chaos Expansions
   Source: stat.CO (Computation)
   Link: https://arxiv.org/abs/2611.09579
   This work develops a locally adaptive jackknife+ conformal prediction framework for polynomial chaos expansions.
14. Quasi-Maximum Likelihood Estimation for a Genuinely Unbalanced Dynamic Network Panel Data Model
   Source: stat.AP (Applications)
   Link: https://arxiv.org/abs/2512.24748
   This paper develops quasi-maximum likelihood estimation for genuinely unbalanced dynamic network panel data models.
15. Box-Cox Transformations of Correlation Matrices
   Source: stat.TH (Statistics Theory)
   Link: https://arxiv.org/abs/2611.09790
   We consider Box-Cox transformations of correlation matrices, $\Phi_\alpha(C)=\mathrm{vecl}\{(C^{\alpha}-I)/\alpha\}$, with Jacobian $J_\alpha$.
16. Circular interval-valued regression with application to wind direction: A cylindrical approach
   Source: stat.CO (Computation)
   Link: https://arxiv.org/abs/2611.09565
   Many real-world datasets are inherently interval-valued, yet standard statistical methods are typically designed for point observations.
17. Exchangeable Gaussian Processes with application to epidemics
   Source: stat.AP (Applications)
   Link: https://arxiv.org/abs/2512.05227
   We develop a Bayesian nonparametric framework for modeling collections of related epidemic trajectories using exchangeable Gaussian processes.
18. Limit laws for the friendship paradox in random geometric graphs
   Source: stat.TH (Statistics Theory)
   Link: https://arxiv.org/abs/2611.09765
   The friendship paradox says that, on average, the neighbours of a vertex have more neighbours than the vertex itself.

Sources in this brief: stat.AP (Applications); stat.CO (Computation); stat.TH (Statistics Theory).
Selected 18 of 45 available items for this weekly brief.