Clay weekly context brief for the Statistics category (ISO week 2026-W38).

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. RDDMPI: Residual Denoising Diffusion Model for Probabilistic Multivariate Time Series Imputation
   Source: stat.ML (Machine Learning)
   Link: https://arxiv.org/abs/2609.11648
   Multivariate time series imputation (MTSI) aims to recover missing values in temporal data composed of multiple interdependent variables.
2. Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale: an offline randomized solution
   Source: stat.CO (Computation)
   Link: https://arxiv.org/abs/1810.04449
   We introduce a Hamiltonian Monte Carlo (HMC) methodology based on an offline empirical calibration of randomized leapfrog parameters.
3. Longitudinal Risk Prediction in Mammography with Privileged History Distillation
   Source: stat.AP (Applications)
   Link: https://arxiv.org/abs/2603.15814
   Longitudinal mammography screening has become an important source of information for improving future breast cancer risk prediction.
4. 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.
5. Real-time and adaptive anomaly detection algorithm for cyclostationary models
   Source: stat.ME (Methodology)
   Link: https://arxiv.org/abs/2609.09326
   This article introduces PeriodicCALM, an effective real-time anomaly detection framework designed for cyclostationary data streams.
6. Housing Price Appreciation, Housing Affordability, and the Spatial Restructuring of Toronto Commuting
   Source: stat.OT (Other Statistics)
   Link: https://arxiv.org/abs/2609.06308
   This study investigates the spatial relationships among housing-market appreciation, housing affordability, and automobile commuting to Toronto across 23 census metropolitan areas and municipalities in southern Ontario from 2016 to 2021.
7. Generalized Score Matching for Parameter Estimation on Convex Domains
   Source: stat.ML (Machine Learning)
   Link: https://arxiv.org/abs/2609.11521
   Maximum likelihood (ML) estimation is a principled and statistically efficient approach for learning probabilistic models.
8. A unified framework for spatially resolved cortical activation analysis
   Source: stat.CO (Computation)
   Link: https://arxiv.org/abs/2609.11278
   Cluster-based permutation tests are widely used for analyzing MEG data, even though they are limited to cluster-level inference and do not provide spatially resolved effect estimates.
9. Precision spectral estimation at sub-Hz frequencies: Closed-form posteriors and Bayesian noise projection
   Source: stat.AP (Applications)
   Link: https://arxiv.org/abs/2507.20846
   We consider the problem of estimating cross-spectral quantities in the low-frequency regime, where long observation times limit averaging over large ensembles of periodograms, thereby preventing the use of approximate Gaussian statistics.
10. On spectral gap decomposition for Markov chains
   Source: stat.TH (Statistics Theory)
   Link: https://arxiv.org/abs/2504.01247
   Multiple works regarding convergence analysis of Markov chains have led to spectral gap decomposition formulas of the form \[ \mathrm{Gap}(S) \geq c_0 \left[\inf_z \mathrm{Gap}(Q_z)\right] \mathrm{Gap}(\bar{S}), \] where $c_0$ is a constant, $\mathrm{Gap}$ denotes the right spectral gap of a reversible Markov operator, $S$ is the Markov transition kernel (Mtk) of interest, $\bar{S}$ is an idealized or simplified version of $S$, and $\{Q_z\}$ is a collection of Mtks characterizing the differences between $S$ and $\bar{S}$.
11. Estimating Causal Treatment Effects in Placebo-Controlled Randomized Clinical Trials When High Placebo Response is Anticipated
   Source: stat.ME (Methodology)
   Link: https://arxiv.org/abs/2609.09377
   In placebo-controlled randomized clinical trials (RCTs), the placebo response significantly modifies treatment effects and diminishes the intention-to-treat (ITT) treatment effect, $\Delta_{ITT}$.
12. Hierarchical Clustering Can Jointly Satisfy Richness, Consistency, and Scale Invariance
   Source: stat.ML (Machine Learning)
   Link: https://arxiv.org/abs/2609.11173
   Despite its ubiquity, clustering lacks a universally accepted definition of what is a cluster.
13. Estimating MCMC convergence rates using common random number simulation
   Source: stat.CO (Computation)
   Link: https://arxiv.org/abs/2309.15735
   This paper presents how to use common random number (CRN) simulation to evaluate Markov chain Monte Carlo (MCMC) convergence to stationarity.
14. Fundamental Properties of Linear Factor Models
   Source: stat.AP (Applications)
   Link: https://arxiv.org/abs/2409.02521
   We characterize the loading matrices that admit a conditional linear factor representation for excess returns in which the factors are traded, residual risk is unpriced, and the loadings are the betas of the factors.
15. Inferring diffusivity from killed diffusion
   Source: stat.TH (Statistics Theory)
   Link: https://arxiv.org/abs/2503.14978
   We consider diffusion of independent molecules in an insulated Euclidean domain with unknown diffusivity parameter.
16. Semiparametric Receiver Operating Characteristic Analysis in the Presence of an Imperfect Reference Standard via a Box-Cox Density Ratio Model
   Source: stat.ME (Methodology)
   Link: https://arxiv.org/abs/2609.09401
   Receiver operating characteristic (ROC) analysis is commonly used to evaluate the diagnostic accuracy of continuous biomarkers.
17. How Wrong Can a Good Predictor Be? Diverging Updates with Vanishing Predictive KL
   Source: stat.ML (Machine Learning)
   Link: https://arxiv.org/abs/2609.11132
   Accurate posterior prediction need not require accurate approximation of Bayesian updates.
18. TrunX: A massively parallel, differentiable implementation of the 3-PG forest growth model in JAX
   Source: stat.CO (Computation)
   Link: https://arxiv.org/abs/2609.02557
   Process-based forest models are widely used to simulate forest growth and responses to environmental change, but their calibration and application often require many computationally expensive model evaluations.
19. Saddlepoint approximations for plug-in resampling
   Source: stat.AP (Applications)
   Link: https://arxiv.org/abs/2407.08911
   Resampling-based procedures can improve on normal approximations in sparse, large-scale testing problems, but their computational cost can be prohibitive.
20. A Bernstein-von Mises Theorem for Generalized Fiducial Distributions
   Source: stat.TH (Statistics Theory)
   Link: https://arxiv.org/abs/2401.17961
   An established and growing literature on generalized fiducial inference and related fiducial ideas points to the adoption of fiducial inference as a mainstream perspective among modern statisticians.
21. Covariate-localized False Discovery Rates
   Source: stat.ME (Methodology)
   Link: https://arxiv.org/abs/2609.09471
   We introduce a flexible model for covariate-dependent multiple testing which can be encoded using a nonparametric Gaussian mixture model.
22. Conformal-DRO: Distributionally Robust Optimization with Conformalized Ambiguity Set
   Source: stat.ML (Machine Learning)
   Link: https://arxiv.org/abs/2609.11073
   Data-driven distributionally robust optimization (DRO) typically treats the conditional outcome law as fixed and uses ambiguity sets to capture estimation error.
23. Context Tree Prior Distributions based on Node Weighting with exact Bayes Factors
   Source: stat.CO (Computation)
   Link: https://arxiv.org/abs/2603.25806
   Variable-length Markov chains (VLMCs) are a flexible class of higher-order Markov models that admit a natural representation as context trees.
24. Sensitivity Bounds and Conservative Inference for Contagion under Latent Homophily
   Source: stat.AP (Applications)
   Link: https://arxiv.org/abs/2606.18197
   Whether connected units are similar because influence spreads across ties or because similar units form ties is a long standing problem.
25. On Existence Theorems for Conditional Inferential Models
   Source: stat.TH (Statistics Theory)
   Link: https://arxiv.org/abs/2301.05135
   The framework of Inferential Models (IMs) has recently been developed in search of what is referred to as the holy grail of statistical theory, that is, prior-free probabilistic inference.
26. Differentially Private Average Treatment Effect Estimation by Propensity Score Blocking
   Source: stat.ME (Methodology)
   Link: https://arxiv.org/abs/2609.09536
   Average treatment effect (ATE) estimation in observational studies is a fundamental statistical tool used frequently in social science, medicine, and other fields.
27. Importance Weighting for Unlabeled-unlabeled Learning under Distribution Shift
   Source: stat.ML (Machine Learning)
   Link: https://arxiv.org/abs/2609.10994
   Unlabeled-unlabeled (UU) learning allows us to learn a binary classifier from two sets of unlabeled data with different class-priors.
28. Identifiability of Latent Space Network Models on Anisotropic Thurston Geometries
   Source: stat.CO (Computation)
   Link: https://arxiv.org/abs/2609.09236
   A latent space network model places the nodes in a metric space and lets the probability of a tie decrease with distance.
29. Trust Me, I'm a Doctor?
   Source: stat.AP (Applications)
   Link: https://arxiv.org/abs/2605.01050
   Clinical trials usually target average treatment effects, but treatment decisions are made for individuals.
30. A note on the distribution of the partial correlation coefficient with nonparametrically estimated marginal regressions
   Source: stat.TH (Statistics Theory)
   Link: https://arxiv.org/abs/1101.4616
   There has been much interest in the nonparametric testing of conditional independence in the econometric and statistical literature, but the simplest and potentially most useful method, based on the sample partial correlation, seems to have been overlooked, its distribution only having been investigated in some simple parametric instances.
31. A binary factor model
   Source: stat.ME (Methodology)
   Link: https://arxiv.org/abs/2609.09587
   The orthogonal factor model has been a very useful tool in uncovering covariance structures in a set of variables through a smaller set of underlying factors.

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 425 available items for this weekly brief.