Statistics Applications Papers of the Week

Modern program evaluation prioritizes evidence from randomized controlled trials (RCTs) because prior research found that observational estimates of program impacts often diverge substantially from experimental benchmarks. We revisit this finding --- which was based on small survey datasets --- in the era of large-scale administrative data.

We compare this approach with adjustment estimated directly from MODIS and Sentinel-3 OLCI observations for European vegetation monitoring.

This paper proposes a Bezier-surface-based Gaussian Process---known as Bezier-GP---for chips-on-wafer warpage modeling.

We evaluate it on 55 acquisitions assisted by the Federal Deposit Insurance Corporation in 2008-2024 by acquirers with a parent listed in the United States.

Yet predictive modeling, machine learning (ML), uncertainty quantification (UQ), and decision-making have been studied as separate capabilities, leaving limited understanding of their integration and maintenance during online operation. This narrative review, scoped by the functional requirements of real-time decision-making rather than by Digital Twin terminology alone, examines how such decisions are generated and sustained in engineering Digital Twins.

Phase I dose-finding trials must learn from sparse toxicity observations while limiting exposure to overly toxic doses.

We outline the evidentiary functions that AI-derived outputs must satisfy before supporting consequential decisions: estimand definition, fit-for-purpose data collection, bias control, uncertainty quantification, validation, interpretability, and decision-grade documentation. Because AI both depends on and accelerates large-scale, non-randomized data collection, the integration of randomized controlled trials (RCTs) with real-world data (RWD) is where its evidentiary risks concentrate; we therefore present a five-step causal roadmap for that integration, in which external data supplement but never replace a concurrent control, and characterize the safeguards required across development, pharmacovigilance, regulation, and health technology assessment.

Adaptive enrichment trials that identify responsive subpopulations allocate a fixed sample across them and estimate each one's effect from the resulting data.

A negative-binomial model describes total counts, while multinomial models describe how those counts are allocated among categories.

Listwise deletion (LWD) appears more common than imputation: our review of leading journals finds that about 50% of articles state or imply LWD, versus 7% describing an imputation model.

We develop a Bayesian framework that infers structural competitive balance from longitudinal match outcomes.

We develop a computation that maintains lower and upper bounds on the ideal endpoint for a prescribed ordering.

Selecting specifications against experimental RoAS benchmarks, even imprecise ones, improves RoAS recovery relative to fit-based selection, and a real-world application with experimental benchmarks shows the same pattern. We propose an applied workflow: calibrate MMM specifications against estimand-aligned experimental benchmarks when they exist, and otherwise report RoAS sensitivity across plausible specifications rather than relying on a single best-fitting model.

The proposed two-dimensional federated smooth multiple-kernel large-margin learning method (2D-FedSMKL) integrates availability-corrected modality scores, group-kernel regularization, and smooth worst-site risk aggregation.

Each location has a battery threshold such that vehicles should return to the depot to recharge when their SoC is below it and should not do so when their SoC is above it. Vehicles should not reposition below the same threshold either; above it, the choice of destination depends on whether a vehicle's SoC matches the destination's battery sensitivity, a notion we introduce in the paper.

This study explores the value of blending the ensemble version of the Artificial Intelligence Forecasting System (AIFS-CRPS) with the Met Office's convection permitting physics-based ensemble, MOGREPS-UK.

The fitted distribution provides probabilities of correct completion, incorrect completion, and remaining unfinished across token budgets, together with expected token consumption. We evaluate the proposed method using responses to MATH questions and demonstrate its value in two downstream applications.

Motivated by a real-world industrial research study, we develop a Gaussian process modeling framework that efficiently analyzes large replicated and censored data.

We propose a heteroskedastic functional mixed-effects regression framework that jointly models the conditional mean and variance of repeated trajectories, with participant-specific random effects in both components.

Applications to single-condition gut microbiota, paired leaf--root microbiota and three-visit human gut multi-omics illustrate the framework across different numbers of conditions.