Papers

Spillover of volatility shocks across borders during turbulent periods makes accurate equity market volatility forecasts especially critical for risk management, derivatives pricing, and regulatory capital. In this paper, we examine whether volatility forecasts improve when models incorporate information on how markets are connected, and whether the choice of connection measure matters. Using daily data on 29 equity indices from every major region over 2015-2025, we let each market's forecast draw on the recent volatility of the markets linked to it, with the strength of each link set either by geography, return correlation, or the Diebold-Yilmaz (DY) spillover network estimated from the data. The spillover-informed forecasting model achieved an approximately 13% reduction in out-of-sample QLIKE loss relative to the standard Heterogeneous AutoRegressive benchmark ($p<0.001$) and attained the highest model confidence set $p$-value among the models considered. Several benchmark models that do not explicitly incorporate cross-market structure were excluded from the 90% model confidence set. The greatest improvements were observed during periods of elevated market stress: the COVID-19 crash, the Russian invasion of Ukraine, and the $2025$ US tariff shock, spillover-informed forecasts achieved approximately 21% lower QLIKE loss than an otherwise identical network-blind model ($p = 0.018$), with no significant performance loss during calm periods. The advantage is economically material: a volatility-targeting investor would pay $322$-$373$ basis points per year, net of transaction costs, for spillover-informed forecasts, versus an insignificant $85$-$102$ basis point for the alternatives. Finally, when the model is left to infer connections on its own, it recovers the DY network from the forecast objective alone (permutation $p=0.0005$).

Joint models provide a flexible framework for characterizing the association between longitudinal and time-to-event processes and have been widely applied in biomedical research. However, fitting joint models can be computationally challenging for large-scale and complex biomedical data. This paper introduces the \proglang{R} package \pkg{FastJM}, which provides computationally efficient frequentist estimation for three classes of semiparametric joint models: joint models with a single longitudinal biomarker, joint models with multiple longitudinal biomarkers, and joint models with a single longitudinal biomarker with heterogeneous within-subject (WS) variability. Within an expectation–maximization framework, \pkg{FastJM} employs customized linear-scan algorithms to efficiently update the nonparametric baseline hazards, thereby addressing a major computational bottleneck in semiparametric joint modeling. The package also supports commonly used time-dependent latent association structures by integrating these algorithms with a landmark multivariate joint modeling framework. \pkg{FastJM} provides a unified interface for model specification, estimation, inference, visualization, dynamic prediction, and prediction performance assessment, including cross-validated time-dependent accuracy measures and time-independent concordance statistics. We describe the underlying methodology and software implementation and demonstrate the main functionality of \pkg{FastJM} through reproducible examples.

When forecasting hourly returns for 1,000 US equities, we observe an unexpected phenomenon: predictions become nearly flat and show poor stock ranking, as measured by cross-sectional correlation. We call this forecast collapse. Surprisingly, the phenomenon largely disappears when forecasting trading volume under the same setting. We investigate forecast collapse across time-series foundation models (TSFMs), twelve deep-learning forecasting models, and 97 public benchmark configurations, and find that it is closely tied to target predictability. We identify two distinct reasons behind it: low predictability limits the amplitude of calibrated point forecasts, while per-series objectives leave cross-series structure unidentified. These findings reveal a calibration-ranking tradeoff: optimizing squared error leads to flat predictions, whereas directly optimizing cross-sectional correlation improves ranking but can inflate forecast amplitude by more than an order of magnitude. To address this tradeoff, we introduce CalibRank, a simple objective that balances calibration and ranking. On Finance1K, CalibRank nearly triples cross-sectional correlation while keeping amplitude close to the target, and improves correlation on all tested models. Our results reveal a blind spot in conventional time-series evaluation: per-series metrics can hide failures in cross-series structure needed by downstream decisions.

Accurate small-area estimation of voter turnout for demographic subgroups is crucial for political analysis but methodologically challenging. Survey data suffer from over-reporting, non-representativeness, and non-ignorable non-response, while ecological inference (EI) from aggregate data is vulnerable to the ecological fallacy. We propose a Bayesian hierarchical framework that jointly integrates individual-level survey data, official aggregate turnout margins, and census cell counts. The framework comprises three models: a multilevel ecological inference (EI) model that extends MRP-style structure to aggregate data, a Poll-and-Margin (PM) model that jointly fits survey and margins in one coherent likelihood, and a Full Selection (FS) model that adds a Heckman-style selection correction under a random-contact assumption and uses informative priors for identification. In simulations with controlled selection mechanisms based on empirical census demographics, FS substantially improves over established benchmarks, particularly under strong selection bias or heavy censoring, but is sensitive to unmodeled selection noise. In an application to the 2023 Estonian parliamentary election, PM performs similarly to the current state of the art on available validation margins, with FS showing a modest improvement that depends on the informative priors.

Timely urban-canopy information is essential for linking remote sensing with heat, mobility, and neighborhood planning. We developed an optical GeoAI workflow for Davis, California, using 2022 National Agriculture Imagery Program imagery (0.6 m RGB+NIR). DeepForest generated crown candidates; an NDVI threshold, non-maximum suppression, and box-prompted Segment Anything Model (ViT-B) produced a crown-anchored canopy surface. Analyses used the 25.92 km2 Census TIGER municipal boundary and a 100 m grid. The workflow retained 11,741 candidate crowns and mapped 7.71 km2 of canopy (29.8% of the city). On the identical extent, pixel precision was 0.804, pixel recall was 0.873, and 97.4% of candidate centers agreed with the 2022 USDA/CAL FIRE LiDAR-assisted canopy product (IoU 0.719; Dice 0.837; area recovery 108.5%). Approximately 49% of candidates occurred within 15 m of a road. Canopy was inversely associated with Landsat land-surface temperature (Spearman rho = -0.477; partial rho = -0.551 controlling for built probability), and spatial-lag modeling confirmed clear neighborhood structure. Two transparent attention surfaces combined canopy need with thermal and contextual indicators. The framework provides a reproducible, updateable screening layer that complements structural canopy products and municipal inventories while retaining assumptions, data provenance, and spatial diagnostics for planning interpretation.

Recovering geographic variation in survival requires separating spatial risk from patients' clinical characteristics, a problem complicated by prognostic covariates that are themselves spatially structured. We develop a nonparametric two-stage method for this separation. A clinical survival tree fit to the covariates alone supplies leaf Nelson-Aalen cumulative hazard residuals, transferring the censored survival structure to a clinically adjusted scale without imposing a functional form on the clinical hazard, and a second tree fit to these residuals on the coordinates recovers the spatial structure. Both stages are kernel dipole-splitting survival trees, so the resulting spatial risk map is piecewise constant, with sharp, possibly curved boundaries. We establish when the residuals recover the spatial signal: under a multiplicative frailty and an exogeneity condition, they are free of the clinical covariates given location and stochastically ordered by the frailty. An expansion of the frailty Laplace exponent quantifies their approximate exponentiality and identifies the leading remainder. These results assume consistency of the first stage rather than a model class, so the construction extends to other survival estimators. On the LeukSurv leukemia data the method agrees with a Bayesian Gaussian random field frailty about where risk is elevated while resolving sharp adjacencies the smooth surface averages away, and an unadjusted spatial analysis misattributes clinical variation to location. Simulations with known zones, including a sweep through graded violations of exogeneity, locate the point at which the two contributions cease to be separately identifiable, with the smooth benchmark degrading in parallel as that point is approached.

Three-player Auction Bridge is a finite imperfect-information game in which dynamic partnerships create a distinctive interaction between scoring, strategic incentives, and payoff distribution. This paper develops a unified statistical and game-theoretic framework to evaluate a traditional scoring rule against a modified mechanism designed to improve strategic incentives. We first identify a structural defect in the original scheme: a bid-invariant slam bonus can make lower contracts strictly more attractive than higher ones. A bid-dependent correction removes this incentive distortion. We then compare the two schemes using higher-moment analysis, fairness measures, Nash equilibrium analysis, and General-Sum Counterfactual Regret Minimization (GS-CFR). The results show significant differences in the shape of the payoff distributions and reveal that, although both schemes remain highly balanced across physical seats, the modified scheme substantially increases inequality across strategic roles. The game is shown to be neither zero-sum nor constant-sum, motivating a general-sum rather than minimax analysis. Full-game GS-CFR further indicates a substantial increase in the bidder's payoff under the modified scheme, accompanied by a reduction in defender payoff and a shift from separating to partially pooling bidding behavior. These findings demonstrate that scoring-rule design can fundamentally reshape incentives, payoff distribution, and information transmission in imperfect-information games. The proposed framework provides a systematic approach to evaluating such mechanisms from both statistical and strategic perspectives.

Joint variability in temperature and precipitation is central in characterizing seasonal climate structure and associated environmental processes, yet many regional analyses rely on marginal or univariate summaries. We analyze seasonal temperature-precipitation patterns across the Southern United States using two complementary multivariate statistical approaches. First, functional multivariate analysis of variance (FMANOVA) is employed to test the equality of state-level bivariate mean functions, with the permutation-based Wilks' lambda and Pillai's trace statistics. Second, Gaussian mixture models are applied to station-level seasonal summaries to identify latent climate regimes based on the joint distribution of temperature and precipitation. The FMANOVA results indicate statistically significant differences in bivariate mean trajectories between states in both winter and summer, with seasonal contrasts reflecting differing contributions of temperature and precipitation. Clustering analysis indicates more clearly defined and spatially coherent winter regimes than summer regimes, with summer regimes exhibiting greater variability and a stronger role for precipitation.

Neurodegenexrative diseases such as Alzheimer's disease and Parkinson's disease are diagnosed most reliably only after substantial, often irreversible, neuronal loss has already occurred, creating an urgent need for quantitative tools that can detect subtle, early, and individual-specific brain changes from neuroimaging data. This review surveys a broad and rapidly evolving toolkit of data-driven techniques for translational neuroscience and personalized neuro-health, organized around four complementary methodological pillars. Throughout, we emphasize how these methodologically diverse approaches converge on a common translational goal: personalized, mechanistically grounded, and clinically actionable models of individual brain health, and we close by discussing the principal open statistical, computational, and clinical challenges that remain.

Area-level small area estimation (SAE) models, such as the Fay–Herriot (FH) model, borrow strength across domains through covariates and random effects, but they can struggle when the relationship between the covariates and the outcome is spatially heterogeneous, that is, when it changes across the spatial domain of interest. We propose a spatially-clustered FH (SC-FH) framework that simultaneously (i) estimates cluster-specific regression coefficients and random effects variances and (ii) generates spatially coherent partitions of the geographical domain. Estimation maximizes a penalized likelihood that augments the FH likelihood with a Potts-type spatial cohesion term over the areal adjacency graph, through an efficient strategy that alternates between sequential label updates and closed-form FH updates within clusters. In simulation experiments run on the real geography of the application, the method recovers the latent regimes almost exactly whenever they are separated in the covariate–response space and improves prediction accuracy over the standard FH benchmark, with the spatial penalty acting as a stabilizer of both classification and estimation. An empirical application to the average standard output of farms in the Po Valley (Northern Italy) identifies two spatially compact production regimes with significantly different cluster-wise coefficients, and shows that the clusterwise predictor improves on the direct estimates while avoiding the over-shrinkage of the pooled model.

Cardiac fibrosis reduces electrical conductivity and is a leading cause of arrhythmia. Arrhythmic waves typically rotate around non-conducting fibrotic patches, so the geometry and topology of these patches (spatially isolated regions of fibrotic tissue within the heart muscle) play an important role in arrhythmia dynamics. Despite their clinical relevance, these structures remain poorly understood. We address this open problem using histopathological images of human hearts affected by cardiac fibrosis. Each patch is represented as a spatial graph via skeletonization, where nodes are embedded as points in Euclidean space and edges encode geometric properties of the underlying tissue. The core methodological contribution of this work is the introduction of spatial graph space, a metric space equipped with a rotation-invariant Fused Gromov Wasserstein metric that enables comparison of spatial graphs with differing numbers of nodes and edges. Building on this, we perform a distribution-level statistical testing and depth measures for spatial graphs. To enable the interpretation of the spatial graph sample distribution, we introduce DepthPlot, a novel visualization tool for depth measures in metric spaces. Applying our methodology to compare patients and the spatial position of patches within the ventricles, we find that fibrotic textures exhibit strong patient-specific features, while some hearts display notable geometric similarities, potentially reflecting shared pathological mutations or other unknown factors. Through quantitative depth measures, we characterize test outcomes via central and peripheral spatial graphs, demonstrating that the proposed framework yields statistically and clinically meaningful insights into fibrotic texture characterization.

We propose a distributed selective inference framework tailored for high-dimensional quantile regression. To enable valid post-selection inference in this context, we address the computational challenge posed by the non-smooth quantile loss via a response-surrogation strategy. This strategy transforms the problem into a penalized least-squares formulation, thereby facilitating distributed selective inference. For valid post-selection inference, a randomized procedure is introduced, in which the Lasso selection event is characterized through the associated Karush-Kuhn-Tucker conditions and the conditional distribution of the aggregated estimator is derived given the selection event. The resulting algorithm requires only three rounds of communication between local machines and the central server. Under standard regularity conditions, we establish the asymptotic validity of the proposed procedure and develop a large-deviation approximation to the selective likelihood for computationally tractable implementation. Simulation studies and a real-data application demonstrate the satisfactory finite-sample performance of the proposed method.

Estimating the distribution of relaxation times (DRT) fromelectrochemical impedance spectroscopy (EIS) is an ill-posed inverse problem that is highly sensitive to regularisation choices. We propose a physics-informed convolutional autoencoder that estimates DRT directly from EIS data without spectrum-specific tuning. A discretised relation between impedance and the DRT is embedded in the training process, constraining the network to produce impedance-consistent distributions. The model resolves overlapping relaxation processes in synthetic two-ZARC spectra and accurately reconstructs measurements from three independent solid oxide fuel and electrolysis cell datasets, with range-normalised errors below 1.1%. Decoder-probe analysis shows that the learned latent representation is organised according to relaxation timescale. Distances in this latent space capture operating changes, hydrogen-shortage events, and long-term degradation. The same lightweight architecture is applied across all datasets without modification, providing consistent DRT estimation and an interpretable basis for condition monitoring.

Spherically invariant (SI) random processes can model impulsive noise and unreliable measurements. Recently, the mixture noise of Gaussian and SI components has been used in deterministic maximum likelihood direction finding. In this context, the Expectation-Conditional Maximization (ECM) algorithm, an extension of the expectation-maximization algorithm, has been applied and designed. However, simulation results show that the ECM algorithm always improperly converges. In this article, the ECM Either (ECME) algorithm, an extension of the ECM algorithm, is applied and designed, which additionally utilizes the actual log-likelihood function to first update partial parameter estimates at every iteration and does not need to initialize all parameter estimates. Moreover, the deterministic Cramer-Rao low bounds (CRLBs) of DOA estimators are derived and compared. Simulation results indicate that the ECME algorithm exhibits proper convergence and its root mean square errors of DOA estimates asymptotically approach the CRLBs as the signal powers increase, i.e., the derived CRLBs are correct.

Socioeconomic indicators in England exhibit complex spatial patterns that are not well captured by standard approaches based on averages or broad geographic classifications. We propose a method for comparing areas based on their internal spatial structure, using a multiresolution representation derived from the discrete wavelet transform. The method embeds areal data into a regular grid, extracts local windows, and represents each as a set of scale- and direction-specific detail coefficients. A dissimilarity measure, defined over these coefficients and minimised over rotations and reflections, is used to identify contiguous sets of statistical units with similar spatial structure. We apply the approach to England's 2025 Index of Multiple Deprivation at the lower layer super output area level. We show that areas with similar internal structure are often found across regions, levels of urbanisation, and average deprivation, challenging the use of these categories as proxies for local geography. The results provide a framework for identifying comparable places based on how deprivation is distributed within them, with implications for policy evaluation and transferability.

In this contribution we study max-stable random fields on the rooted tree under shifts to descendant subtrees. Branch-Brown–Resnick stationarity is characterised through homogeneous spectral classes, punctured tail measures, and local spectral tail fields. For lognormal representers, it is equivalent to invariance of the variogram under addition of a common prefix. We give Gaussian, max-autoregressive, regenerative cascade, and free-group cluster constructions, and show that summability on countably branching trees need not satisfy a zero–one law. We also derive the associated branch-invariant extreme-value and Archimax copulas.

The two-stage approach and Bayesian joint modeling are commonly used to analyze longitudinal biomarker measurements together with time-to-event outcomes. Using data from an HIV clinical trial of 467 patients with repeated CD4 measurements and all-cause mortality as the survival outcome, we compared a baseline two-stage approach with a Bayesian joint model. The two-stage analysis fitted a linear mixed-effects model and included each patient's predicted baseline CD4 value as a fixed covariate in a Cox proportional hazards model. The joint model simultaneously modeled the longitudinal CD4 process and survival while linking mortality risk to the current underlying CD4 value. The estimated association between ddI and mortality was similar in direction and magnitude across the two approaches. The two-stage estimate was HR = 1.342 (95% CI: 1.006-1.789), whereas the joint-model estimate was HR = 1.383 (95% CrI: 0.952-2.010). The estimated protective association of CD4 was stronger under the joint model (HR = 0.776) than under the two-stage approach (HR = 0.826). Because the approaches used different summaries of the longitudinal CD4 process, the observed differences cannot be attributed solely to measurement error or informative dropout. The findings demonstrate that the treatment of longitudinal biomarker information can materially affect statistical inference.

Cramer's V is the effect size reported beside almost every chi-square test, read against Cohen's labels, yet a large fraction of such numbers describe sampling noise and the standard bias correction (Bergsma 2013) does not fix it. We assemble three results and claim only the consequence of each. First, the squared effect size phi^2 admits no unbiased estimator at any sample size: under fixed-N multinomial sampling the expectation of any estimator is a polynomial in the cell probabilities, while phi^2 is not. Second, unbiasedness transfers across a rescaling of the effect size only if the rescaling is affine, and among affine choices V^2 = phi^2/k is the one bounded in [0,1] with value 1 at perfect association. Third, we give an interval with conservative, asymptotically valid coverage, obtained by projecting a likelihood-ratio confidence set for the cell probabilities through the effect-size map; its lower endpoint is zero in closed form exactly when the test of independence fails to reject. Because nothing is unbiased, the only question is where the irreducible error is placed. Bergsma's correction puts zero error at the null but several percent under the alternative; a delete-one jackknife on the V^2 scale spreads it thin everywhere (absolute bias at most 0.008 over a 180-design grid). Pooling does not remove bias: across 3,000 simulated meta-analyses, pooling 200 studies drives the naive estimator's chance of landing within 0.01 of the truth to zero, while the jackknife's rises to 0.99. The projected interval covers 0.997-1.000 across the tested designs, while a noncentral inversion undercovers (0.936 at phi^2 = 0.18) at two to three times the width. The point estimate and the interval are different problems with different answers; conflating them is why the literature has neither. We give a routing rule and a browser tool that implements it.

Wildfire susceptibility mapping typically relies on physical variables assembled from multiple remote-sensing, climate, and geospatial products. AlphaEarth Foundations (AEF) provides analysis-ready geospatial embeddings that may reduce this dependence on heavy harmonisation and task-specific feature engineering, but their value for wildfire susceptibility mapping has not been systematically evaluated. Using Victoria, Australia (2017-2025), as a case study, we show that AEF embeddings can reconstruct commonly used variables in wildfire susceptibility analysis with high accuracy. In downstream susceptibility models trained on satellite-derived fire occurrence data, embedding-based susceptibility models achieve ROC-AUC values above 0.92 and consistently identify high wildfire susceptibility across eastern Victoria, particularly Gippsland and the north-eastern uplands, with additional localized hotspots in central and northwestern Victoria. A key feature of AEF embeddings is their strong near-region transferability within climatically similar regions. When embedding-based models trained in Victoria are applied to Canberra and Western Sydney-Blue Mountains, ROC-AUC improves by around 4% at Canberra and declines by around 2% at Western Sydney-Blue Mountains, compared with a mean decrease of approximately 25% for physical-variable models. These findings provide practical guidance for using AEF embeddings and lay a foundation for scalable wildfire susceptibility mapping workflows for downstream users such as government agencies and (re)insurers.

We propose a hierarchical Bayesian model for estimating time-varying outage risk from county-level power outage data. The model combines cubic B-spline basis functions with a Beta-Binomial likelihood to capture smooth, nonlinear recovery trajectories while accommodating overdispersion in observed customer counts. A shared hyperprior on the Beta-Binomial concentration parameter enables hierarchical shrinkage across geographically indexed groups, allowing sparse or short-lived events to borrow statistical strength from the broader population. Posterior inference is conducted via the No-U-Turn Sampler (NUTS) in PyMC, yielding full posterior distributions over latent outage probabilities and derived resilience metrics including the area under the risk curve (AUC). We assess predictive performance using posterior predictive coverage, RMSE, and leave-one-out cross-validation, and demonstrate the model across a heterogeneous set of outage events in southern Wisconsin drawn from the EAGLE-I power outage monitoring platform. A direct comparison against naive trapezoidal AUC estimation confirms that the posterior mean recovers the same point estimates as deterministic integration while providing calibrated uncertainty quantification that deterministic approaches structurally cannot. The framework offers utilities and emergency planners a principled tool for benchmarking recovery dynamics and comparing outage events under uncertainty.

We present a hierarchical Bayesian framework for non-homogeneous pairwise interaction Gibbs point process models, where the global and local effect functions are modeled via basis function expansions. We further propose a testing procedure in order to assess complete spatial randomness. The proposed methodology is exemplified through two real benchmark data examples involving water striders and forest fires.

Detection and attribution of climate change relies on fingerprinting–a linear errors-in-variables regression framework in which both predictors and responses exhibit internal variability governed by a proportional covariance structure, subject to a variability inflation factor. Accurate estimation of the scaling factors (regression coefficients) depends on inferring the precision matrix of the regression errors from limited climate model control runs. In high-dimensional settings, existing approaches often overlook the variance inflation of the predictors and suffer from imprecise precision matrix estimates, yielding biased estimators, underestimated uncertainties, and confidence intervals with poor coverage. We propose a nonlinear, rotation-invariant shrinkage framework for estimating the precision matrix that restores the asymptotic optimality of the total least squares estimator in high-dimensional regimes. Our procedure jointly estimates the scaling factors and the variability inflation factor, thereby correcting estimation bias, and incorporates consistent variance estimators to enable valid uncertainty quantification. We also develop a residual consistency test to assess model adequacy. Numerical studies demonstrate precise estimation, improved confidence interval coverage, and higher efficiency. Applied to annual mean near-surface air temperature data from 1951–2020, our method produces narrower and more reliable confidence intervals, yielding refined attribution results.

Two free satellite signals carry real information about glacial-lake outburst risk in the Nepal Himalaya: radar interferometry sees a moraine dam slowly sagging, and satellite weather marks the weeks when a primed lake is under stress. A companion feasibility study found that deformation indicates which lake is destabilizing and weather indicates when it is at risk, but proposed no predictive model. To address this gap, we propose and evaluate models that predict which site is susceptible and when a trigger arrives. We test three related hazards on free data alone: large moraine- and ice-dammed bursts, rainfall-triggered landslides, and smaller floods from ponds on and around a glacier. Each hazard gets two questions, never blended. Using 589 dated outbursts from HMAGLOFDB and several thousand catalogued landslides, we match each event against similar but unfailed sites, and hold every model to a strong simple baseline under spatial cross-validation that withholds whole map tiles, so no model succeeds by recognising a trained-on neighbourhood. Antecedent weather times the trigger at ROC 0.73 for big bursts, 0.83 for landslides, and 0.82 for small floods. Terrain ranks susceptibility only in part: scored naively it appears near 0.9, largely because catalogued failures cluster in wetter ranges; matched against comparable nearby sites the honest figures are 0.76, 0.71, and 0.54 (no better than chance). The burst signal holds within single regions, reaching 0.89 in Nepal alone. Five deep-learning models do not decisively beat a simple gradient-boosted baseline. Three score marginally higher on landslides, a hint too small to confirm. For the lake hazards the baseline wins outright, reproduced by a three-rule decision tree on ruggedness and monsoon rainfall. We close with a ranked Nepal watchlist, a prioritisation aid, not a prediction, and note where free data reaches its limits.

What a city makes reachable depends less on what it contains than on who can still afford to move when travel costs rise. We leverage the 2026 US-Iran oil shock as a natural experiment, applying a hierarchical panel regression discontinuity design to 1.7 trillion point-of-interest visits across 122,000 neighbourhoods in China and the United States. Mobility range declined in nearly three-quarters of neighbourhoods, but responses varied systematically with pre-shock urban conditions. Exposure to energy-intensive travel explained the largest share of modelled heterogeneity in both countries, while adaptive capacity and activity composition further shaped how travel was reorganized. Longer baseline travel intensified contraction, whereas greater car dependence constrained adjustment. Crucially, similar mobility outcomes arose from different processes: some neighbourhoods maintained travel by absorbing higher costs, whereas others appeared structurally locked into travel they could not reorganize. Fuel-price shocks, therefore, act as urban stress tests, revealing which neighbourhoods a city keeps connected.

The presence of unmeasured confounding factors during the collection of observational data may lead to biased estimates of the effect of an exposure on an outcome. Consequently, a central problem in causal inference based on observational data is sensitivity analysis with respect to unmeasured confounding. Existing sensitivity analyses generally focus on worst-case bounds. We propose an exact method for quantifying the significance of confounding, defined here in terms of the complete range of analytical estimates of the stratification-based Risk Ratio over the set of all joint distributions compatible with the observed characteristics. We refer to the proposed method as Inverse Confounding Analysis (ICA). The proposed ICA method extends the widely used E-value approach but, in contrast to it, does not restrict the analysis to a worst-case lower bound. Instead, it provides exact estimates over the entire set of admissible configurations. This requires several additional input parameters, namely the frequencies of the exposure, the confounder, and the outcome. The ICA method is based on an inverse problem: reconstructing the set of admissible joint distributions from specified frequencies and pairwise associations. We formulate this reconstruction problem as a system of nonlinear equations and obtain an analytical solution. Surprisingly, the complete solution set can be parameterized linearly by a single free parameter. The corresponding stratification-based Risk Ratio is then represented as a fractional-linear function of this parameter. This representation makes it possible to derive exact analytical measures of the significance of confounding over the entire set of admissible statistical configurations.

Efficient urban logistics requires a comprehensive understanding of traffic dynamics, particularly as it pertains to kinetic parameters influencing energy consumption and trip duration estimations. While real-time traffic information is increasingly accessible, current high-precision forecasting services embedded in route planning often function as opaque 'black boxes' for users. These services, typically relying on AI-processed counting data, fall short in accommodating open design parameters essential for management studies, notably within Supply Chain Management. This work revisits the modelling of traffic conditions in the context of city logistics, emphasizing its significance from the user's point of view, with two focuses. Firstly, the focus is not on the vehicle flow but on the vehicles themselves and the impact of the traffic conditions on their driving behaviour. This means opening the range of studied indicators, beyond vehicle speed, to describe extensively the kinetic and dynamic aspects of the driving behaviour. To achieve this, we leverage the Art.Kinema parameters designed to characterizing driving cycles. Secondly, this study examines how the driving context (i.e., exogenous factors to the traffic flow) determine the mentioned driving behaviour. Specifically, we explore how accurately the kinetic behaviour of a vehicle can be predicted based on a limited set of exogenous factors, such as time, day, road type, orientation, slope, and weather conditions? To answer this question, statistical analysis was conducted on real-world driving data, which include high-frequency measurements of vehicle speed. A Factor Analysis and a Generalized Linear Model have been established to link kinetic parameters with independent categorical contextual variables. The results include an assessment of the adjustment quality and of the robustness of the models, as well as an overview of the models' outputs.

The Global Carbon Budget (GCB), the community reference dataset for the carbon cycle, is reissued annually. The 2025 release introduces several adjustments to the published series that we compare with prior releases starting in 2017. On a common 1959-2016 sample, the mean of the GCB budget imbalance jumps from within +/-0.17 GtC/yr of zero for every vintage 2017-2024 to 0.61 GtC/yr in 2025, the only vintage whose 95% confidence interval for the imbalance mean excludes zero. The size of the imbalance changes much less: its mean absolute value rises from 0.61 to 0.76 GtC/yr. It is the mean, the quantity the budget identity constrains, that moves. We document and explore this shift in two ways. First, we conduct a model-free analysis, where we attribute the shift to a new adjustment that places the published land sink 0.40 GtC/yr below its ensemble mean (the average of the underlying models), a smaller adjustment in the ocean sink in the opposite direction, and a change in the composition of the bookkeeping ensemble. Second, we consider a dynamic statistical GCB model augmented with climate covariates. Its parameters are estimated for every GCB vintage 2017-2025. The coefficients of atmospheric concentrations in the sink equations shift in opposite directions on the 2025 issue, mirroring the model-free findings. A constant in the budget equation, statistically unnecessary in every vintage from 2017-2024, is required in 2025 and is estimated at -0.59 (0.09) GtC/yr. There is a persistent drifting imbalance across the entire sample in the budget equation. Each of the three adjustments is documented in the 2025 release and rests on evidence about the component it corrects. Their joint effect is a budget that closes over the last ten years and carries a mean imbalance of 0.61 GtC/yr over the full record. We argue that this cost to the full sample outweighs the gain on the last ten years.

Digital light processing (DLP) enables rapid fabrication of polymer structures, but fracture performance depends on multiple interacting processing variables, making exhaustive experimental characterization impractical. This work presents a data-efficient framework for process-fracture mapping of a DLP-printed photopolymer using Bayesian active learning and digital image correlation (DIC)-assisted Mode I fracture experiments. Four processing parameters were considered: layer angle, UV exposure time, layer height, and print temperature. Fracture resistance was quantified by the critical J-integral, $J_c$, obtained from three-point-bending tests with DIC-based evaluation of crack-mouth opening displacement and hinge-point kinematics. Beginning with two randomly selected conditions, Gaussian process regression (GPR) and a modified upper confidence bound (UCB)-style acquisition function selected 26 additional experiments, yielding 28 processing conditions with three replicates each. The final GPR surrogate reproduced the training data with $R^2=0.99$ and achieved leave-one-out cross-validation performance of $R^2=0.63$ and Pearson $r=0.81$. Surrogate-based sensitivity analysis quantified parameter effects and global contributions. One-at-a-time response curves revealed nonlinear conditional trends, while global Sobol analysis identified UV exposure time as the dominant processing variable, with first-order and total-order indices of 0.6780 and 0.7581, respectively. Based on total-order influence, the parameters ranked as UV exposure time, layer angle, print temperature, and layer height. The first-order Sobol indices summed to 0.8058, indicating non-negligible interaction and higher-order effects. These results demonstrate that Bayesian-active-learning-guided experimentation can efficiently recover process-fracture relationships and parameter interactions from a sparse experimental campaign.

Practitioners enrich customer-return models with ever more signals (lifetime value, category, recency/frequency, calendar, geography), and the temporal-point-process (TPP) literature follows suit with covariate- and external-covariate-conditioned intensities. But does any of it improve the timing, and how would you know? A null ("feature X doesn't help") is only meaningful if the model could have found a signal. We make two contributions–a method and a measurement–to answer this credibly. (i) A screen-and-confirm protocol that certifies whether a candidate signal improves a TPP's event-timing likelihood: a positive control plants a coupling of known strength and confirms the model recovers it, so a real-data null can be read as "no signal" rather than "weak method." The control is validated for categorical and continuous encodings, and on a real clock-driven dataset (NYC taxi hour-of-day). (ii) A model-free ceiling quantifying how little of customer-return timing is point-predictable at all (a single-digit percentage of gap variance from any covariate; returns are near-memoryless). With these we certify a clean result on three public benchmarks (Amazon, Taobao, RetailRocket) and a real marketplace (Thumbtack): the inter-event clock–continuous-time decay, long known to beat frozen-intensity models–is nearly sufficient, and the conditioning the field keeps adding is redundant or harmful on top of it (statistically null on the public benchmarks, at most 0.06 NLL; null to mildly harmful on the marketplace). We do not claim to discover that decay helps; our contribution is the tools that turn "conditioning doesn't help" into a checkable, certified statement–plus an honest-evaluation account of the read-out/leakage pitfalls we hit and retracted.

The continuous calibration of pilot items embedded in operational assessments is challenging when pilot samples are small and adaptively routed. We formalize a Bayesian modularization framework for local item calibration that blocks feedback from pilot responses to the operational latent scale: Plausible Values are drawn from the operational posterior, and each pilot item is calibrated by its own local logistic regression. This construction is computationally scalable, protects operational trait estimates from malfunctioning pilot items, and admits Firth's penalized likelihood for sparse routed samples. Because treating imputed traits as fixed predictors induces attenuation, we derive closed-form disattenuation mappings that recover the generating item parameters under normal-ogive, posterior-normality, homoscedasticity, and joint-normality approximations. The resulting Modular Local Calibration (MLC) estimator reaches the target of marginal-likelihood calibration without per-item numerical integration. A multivariate delta-method covariance combined with Rubin's-rules pooling propagates operational item-parameter uncertainty into the focal item standard errors. Monte Carlo simulations for the unidimensional 2PL show that corrected MLC substantially reduces attenuation bias and yields nominal-to-conservative interval coverage in the studied conditions, including under restricted-range MAR routing.