582 papers
Carbon cost pass-through rate in power system: evidence from Italy under the EU ETS
This paper investigates the impact of carbon pricing under the EU Emissions Trading System (EU ETS) on the Italian electricity market, focusing on the carbon cost pass-through rate (CPTR) across market zones during Phases 3 and 4 (2016-2024). Using daily data, the study applies an econometric framework based on a linear regression model with autoregressive dynamics to estimate the extent to which carbon costs are reflected in wholesale electricity prices. It further incorporates robustness checks and quantile regression to assess how the CPTR varies across different fuel spread levels. The results show that carbon costs are positively and significantly transmitted to electricity prices, confirming the relevance of carbon pricing as a key market driver. However, pass-through is incomplete, with CPTR values consistently below 100%. At the national level, the CPTR remains relatively stable at around 30% across the two phases. Substantial heterogeneity emerges across market zones: pass-through increases in the North, Centre-North, and Sardinia during Phase 4, while it declines in the Centre-South and Sicily, reflecting differences in generation mix, carbon intensity, and market conditions. Overall, the findings highlight the importance of market zones factors in shaping the effectiveness of carbon pricing in electricity markets.
2604.03076Apr 2026
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Wasserstein-Based Test for Empirical Measure Convergence of Dependent Sequences
We develop Wasserstein-based hypothesis tests for empirical-measure convergence in stationary dependent sequences. For a known candidate invariant measure $μ$, we study the statistic $T_n=\sqrt{n}\,W_1(\hatμ_n,μ)$ and establish asymptotic level-$α$ validity under the null, together with consistency under fixed alternatives. When the invariant measure is unknown, we derive the asymptotic law of the pairwise statistic $\sqrt{n}\,W_1(\hatμ_n^{(i)},\hatμ_n^{(j)})$ for independent trajectories and obtain a corresponding pairwise test, including Bonferroni control for multiple comparisons. Simulation experiments involving both linear and nonlinear dynamical settings illustrate both the coverage probability and the power of the tests.
2604.02700Apr 2026
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Applied Statistics Requires Scientific Context
Statistical methods are indispensable to scientific inference. However, there exists a longstanding tension across a wide range of scientific disciplines about the role that ``context'' should play in the application of statistical methods and the interpretation of statistical results. Though frequently invoked, the notion of ``scientific context'' refers to at least two distinct concepts: a set of foundational nuanced and elusive background assumptions and substantive features of a given area of study that shape the validity and reliability of statistical methods; and more quantifiable contextual issues that affect the performance of statistical methods and interpretation of statistical results. I argue here that the application and interpretation of statistical methods requires careful consideration of foundational contextual issues. To motivate the arguments, I review a recent re-formulation of the $p$-value as a measure of divergence between an observed dataset and a set of assumptions used to construct statistical measures. I use this framework to illustrate the role that context plays in two randomized trials: on low-dose aspirin for pregnancy loss, and a new inhibitor of a key biochemical pathway affecting ankylosing spondylitis. Finally, I note that the adoption of low significance thresholds in genome-wide association studies and high energy particle physics has been successful more so because of extensive validity-checking gauntlets and contextual considerations that have accompanied these low thresholds, not because of the low thresholds themselves. I use these illustrations and arguments to suggest that (i) the adoption of a universal threshold for significance testing should be abandoned as a goal of statistics reform; and (ii) the validity and optimal use of applied statistical tools requires careful consideration of nuanced scientific context.
2604.02526Apr 2026
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Possible, Yes; Ignorant, Perhaps: A Scorecard for Possibilistic Forecasts
Probabilistic forecasts must sum to unity and cannot express ``I don't know.'' Possibility theory relaxes this constraint: a subnormal distribution explicitly measures how much of the plausibility budget remains unassigned, ignorance signal that probability cannot represent. This paper develops a verification framework for such forecasts, centred on a five-number scorecard that separately diagnoses whether the forecast pointed at the right outcome (depth-of-truth), how sharply (diffuseness, support margin), how confidently (ignorance), and how dominantly (conditional necessity). A possibility-to-probability conversion preserves ignorance for familiar frequency-based scoring; categorical threshold scores (POD, FAR, CSI, etc.) connect to operational practice. Together, these three complementary facets -- possibilistic, probabilistic, and categorical -- expose failure modes invisible to any single metric. Storm Prediction Center convective outlook categories serve as the running example throughout; a synthetic reforecast demonstrates diagnostic visualisations and scorecard interpretation. Ignorance is better expressed than repressed.
2604.02187Apr 2026
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Varying risk exposure in auto insurance: a weighted tweedie framework for experience rating an cancellation penalties
This paper proposes a new family of Tweedie-based ratemaking models that explicitly account for mid-term policy cancellations. Using an automobile insurance dataset from a Canadian insurer, we document a marked difference in claims experience between policyholders who maintain their coverage until maturity and those who cancel their policies mid-term. Building on the classical Tweedie framework, we introduce flexible weighting functions and a premium penalty structure that depend on the level of exposure, allowing for a more realistic representation of the earned premium when coverage is interrupted before the end of the policy period. We compare several weighting structures within the Tweedie framework and examine their theoretical properties, as well as their empirical performance using deviance-based model comparison criteria, an area-between-curves criterion derived from concentration and Lorenz curves, and Murphy diagrams grounded in Bregman dominance. To operationalize the proposed models, monotonicity and non-negativity constraints are imposed on the penalty function, ensuring consistency with actuarial principles. Finally, using real-world data, we show that this approach provides both a strategic and competitive advantage: it allows the insurer to indirectly compensate for large losses through a cancellation surcharge, while preserving actuarial coherence and statistical consistency.
2604.02400Apr 2026
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Country-wide, high-resolution monitoring of forest browning with Sentinel-2
Natural and anthropogenic disturbances are impacting the health of forests worldwide. Monitoring forest disturbances at scale is important to inform conservation efforts. Here, we present a scalable approach for country-wide mapping of forest greenness anomalies at the 10 m resolution of Sentinel-2. Using relevant ecological and topographical context and an established representation of the vegetation cycle, we learn a predictive quantile model of the normalised difference vegetation index (NDVI) derived from Sentinel-2 data. The resulting expected seasonal cycles are used to detect NDVI anomalies across Switzerland between April 2017 and August 2025. Goodness-of-fit evaluations show that the conditional model explains 65% of the observed variations in the median seasonal cycle. The model consistently benefits from the local context information, particularly during the green-up period. The approach produces coherent spatial anomaly patterns and enables country-wide quantification of forest browning. Case studies with independent reference data from known events illustrate that the model reliably detects different types of disturbances.
2604.02074Apr 2026
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Correlation analysis of the dispersion of SARS-CoV-2 in Mexico
In this paper, we propose a method to analyze correlations in pandemic-related data across different geographical regions, relying on the analysis of correlations for non-stationary time series, which are typical of pandemic data. Unlike traditional epidemiological approaches focused on medical and modeling perspectives during a pandemic, our method emphasizes post-pandemic analysis to assess how societal responses; such as lockdowns, travel restrictions, mobility patterns, and vaccination campaigns, manifest in the collective behavior of regions. These insights can inform future public health strategies and enhance understanding of the complex dynamics underlying pandemic spread and control.
2604.01735Apr 2026
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Opponent-Adjusted Evaluation of NFL Pass Blocking and Pass Rushing Performance
Evaluating offensive linemen and pass rushers at the player level is difficult because observable outcomes are sparse, opponent-dependent, and strongly shaped by surrounding context. Using 2021 regular-season Hudl tracking data, we construct a blocker-rusher interaction dataset and estimate two ridge-regularized Bradley-Terry paired-comparison models: a binary win/loss model aligned with the 2.5-second pass block win-rate definition and a four-class severity model over loss, win, hit, and sack, with both models incorporating a double-team indicator. The final dataset contains 153,138 interactions across 33,283 pass plays in 266 games. On an ordered 80/20 holdout split (test n = 30,628), both models improve on global baselines and modestly outperform stronger matchup baselines under log-loss evaluation, corresponding to relative log-loss reductions of about 0.24% to 1.21%. Game-level bootstrap resampling indicates that these gains are most stable for the win model and for the severity model relative to the global baseline, while the severity-versus-matchup comparison remains directionally positive but less certain. External comparison to 2021 AP All-Pro selections provides additional face validation on the learned rankings, with the severity model showing the strongest alignment to expert recognition. Overall, ridge-regularized Bradley-Terry models provide an interpretable opponent-adjusted framework for evaluating NFL pass protection and pass rush at the interaction level.
2604.01491Apr 2026
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Beyond the Beta Lorenz Curve: A New Parametric Family for Poverty and Inequality Estimation
The estimation of inequality and poverty measures is frequently constrained by a lack of individual data. Many countries, including China, continue to report income data in the form of aggregated income shares. In this context, the Beta Lorenz curve, introduced by Kakwani (Econometrica, 48, 1980), has become a standard tool for reconstructing income distributions at both academic and institutional levels. Notably, alongside the General Quadratic (GQ) Lorenz curve, it represents the primary specification used by the World Bank to construct its official poverty estimates when microdata is unavailable. In this paper, we demonstrate that Kawani's model fails to satisfy the formal requirements of a genuine Lorenz curve. To address this, we identify the specific constraints that ensure the theoretical validity of this model and introduce a new family of Lorenz curves derived from the corrected parametric space. Our analysis, conducted across more than 2,000 datasets, reveals that our proposed four-parameter specification provides highly accurate estimates of several poverty and inequality measures. Our results show that this model consistently outperforms the GQ Lorenz curve, which we find tends to underestimate poverty in over 80 percent of the analyzed cases
2604.00772Apr 2026
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Feature Reconstruction and Monitoring of Load Test Data under Varying Environmental Conditions
System outputs in Structural Health Monitoring (SHM), such as sensor measurements or extracted features like eigenfrequencies, are influenced not only by (potential) damage but also by environmental and operational variables (EOV). Identifying these factors and removing their effects from the data is essential before proceeding with further analysis. Most existing methods for this task focus on the expected values of system outputs, e.g., using different types of response surface modeling. However, it has been shown that confounding variables can also affect the (co-)variance of and between system outputs. This is particularly important because the covariance matrix is an essential building block in many damage detection methods in SHM. Beyond standard response surface modeling, a nonparametric kernel approach can be used to estimate a conditional covariance matrix that can change depending on the identified confounding factor. This improves our understanding of how, e.g., temperature affects the system outputs. In this work, we present a new confounder-adjusted version of feature reconstruction. It uses the conditional covariance matrix as the basis for (conditional) principal component analysis. The resulting (conditional) principal component scores are then used to reconstruct system outputs with the confounding influences removed. In particular, the new approach eliminates the confounders effect on both the mean and the covariance. As will be shown on load test data from the Vahrendorfer Stadtweg bridge in Hamburg, Germany, the reconstructed features can then be employed for monitoring, e.g., using an appropriate control chart, resulting in fewer false alarms and a higher probability of detecting damage.
2604.00662Apr 2026
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Spatiotemporal Characterization of Overdose Mortality in Georgia, USA Using Spectral and Nonlinear Interaction Analysis, 2003-2021
Drug overdose mortality in the United States exhibits strong geographic heterogeneity and complex temporal evolution, yet most spatiotemporal studies focus on trends and risks without explicitly characterizing the underlying dynamical structure of overdose trajectories. We develop a nonlinear spectral-spatiotemporal framework to analyze county-level overdose mortality in the state of Georgia from 2003 to 2021. Annual mortality rates are decomposed into low- and high-frequency components to distinguish long-term epidemic pressure from short-term variability, and nonlinear cross-frequency interaction is quantified using bispectral intensity. Counties are grouped into spectral phenotypes using unsupervised clustering, and single-breakpoint change-point models are used to identify regime shifts and quantify post-break acceleration across phenotypes. We find that overdose dynamics across Georgia are dominated by persistent low-frequency growth with limited independent short-term volatility. Nonlinear amplification is spatially concentrated and co-occurs with strong long-term epidemic pressure. Despite synchronous statewide breakpoints around 2014, post-break growth accelerates most sharply in counties exhibiting high low-frequency power and elevated nonlinear interaction. Together, these results provide a mechanistically interpretable framework for identifying dynamical risk phenotypes and structural transitions in spatial overdose epidemics.
2604.00353Apr 2026
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A Rank-Based Information Fusion Framework for Comparing Clustered Multivariate Socioeconomic Outcomes
We propose a multivariate, distribution-free ranking framework for comparing clustered, correlated outcomes across groups, motivated by the evaluation of state-level policy environments using county-level socioeconomic data. Using pooled U.S. county data from 2019-2023, we study multiple dimensions of economic well-being, including poverty, income inequality, housing cost burden, medical care costs, and per capita income, observed at a finer spatial resolution than the policy itself. Rather than relying on parametric regression models, we employ a rank-based aggregation algorithm derived from the Longitudinal Rank-Sum Test (LRST), which treats clusters as independent units and aggregates information across outcomes using order statistics. This approach provides a robust, interpretable omnibus comparison that accommodates within-cluster dependence and high-dimensional outcome structure without distributional assumptions. Applied to the comparison of states with and without refundable Earned Income Tax Credit (EITC) policies, the method reveals systematic differences in the joint ranking of county-level outcomes, with results remaining stable under repeated random subsampling of counties and varying cluster sizes. While the empirical analysis is descriptive rather than causal, the study highlights the broader utility of rank-based, multi-criteria aggregation methods as computational intelligence tools for analyzing complex, clustered data in policy and social systems.
2604.00330Apr 2026
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A Bayesian Finite Mixture Model Approach for Mixed-type Data Clustering and Variable Selection with Censored Biomarkers
Clustering mixed-type data remains a major challenge in biomedical research to uncover clinically meaningful subgroups within heterogeneous patient populations. Most existing clustering methods impose restrictive assumptions like local independence, fail to accommodate censored biomarkers, or unable to quantify variable importance. We propose a Bayesian finite mixture model (BFMM) clustering framework that addresses these limitations. BFMM flexibly models both continuous and categorical variables, incorporates three covariance structures to capture cluster-specific dependencies among continuous features, and handles censored observations through likelihood-based imputation. To facilitate feature prioritization, BFMM uses spike-and-slab priors to estimate variable importance on a continuous 0-1 scale. Simulation studies demonstrate that BFMM outperforms existing methods in clustering accuracy, particularly given strong within-cluster correlation or censored variables, and reliably distinguishes informative features from noise under varying conditions. We applied BFMM to two real-world datasets: (1) the SENECA cohort integrating electronic health records from patients with Sepsis; and (2) the EDEN randomized trial of patients with acute lung injury. In both settings, BFMM identified clinically interpretable phenotypes and revealed variable-specific contributions to subgroup differentiation. In the EDEN trial, it also uncovered evidence of treatment heterogeneity. These findings validate BFMM as an effective, interpretable, and practically useful clustering tool for complex biomedical datasets.
2603.29316Mar 2026
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The Problem of Dynamic Spatial Sampling and Geofence Surveillance
Geofencing surveillance poses a dynamic spatial sampling problem. Law enforcement must establish geofence perimeters to identify a relevant suspect. This requires identifying a sampling region around a surveillance site and counting the number of intersecting individuals as proxied by geolocation tags. Law enforcement commonly constructs sampling regions with fixed distance intervals or fixed polygon boundaries. This generates privacy concerns as considerations for constructing these perimeters do not factor in the local density of human activity, such as pedestrian flows or traffic patterns. This increases the risk of selective expansion where agencies attempt to extend their data collection beyond what a warrant previously approved. This paper attempts to balance law enforcement's needs for surveillance with individual level privacy by proposing a set of optimal radius estimators. These plug-in estimators use the empirical distribution of human activity patterns to estimate an optimal radius. Given a surveillance site and set of point densities, the optimal radius generates surveillance perimeters that adapt to local conditions. We discuss the implications of applying this estimator to policing surveillance efforts and how law enforcement can use algorithms to better protect the privacy of its citizens.
2603.28958Mar 2026
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GPU-accelerated Bayesian inference for block-cave mine monitoring via muon tomography
We describe a Bayesian framework for an inverse problem arising from monitoring block caving operations via muon tomography. We work with a low dimensional surface-based representation of the geometry of the block cave, which dramatically reduces the computational requirements of the model while allowing realistic geometries. Adopting a Bayesian approach, we define a prior distribution on the space of geometries that favors realistic cave shapes. Pairing this prior with a likelihood based on the muon tomography forward model, we obtain a posterior distribution over cave geometries using Bayes rule. We obtain approximate samples from this posterior distribution using Markov chain Monte Carlo algorithms running on GPUs, resulting in fast and accurate sampling. We test the fidelity of our methodology by applying it to a simulated block caving scenario for which the ground truth is known. Results show that our method produces a diverse array of sensible geometries that are simultaneously compatible with the data.
2603.28907Mar 2026
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Violence Against Women: a pilot study on the perception of Apulian High school students
Violence Against Women (VAW) is a widespread issue deeply rooted in social and cultural structures. Affecting women of all ages and backgrounds, VAW is often underreported due to stigma and victim-blaming. This study explores young people's perceptions of VAW in the Apulia region (Southern Italy), using a local survey inspired by a National framework on gender stereotypes and attitudes towards VAW. The survey gathers insights into youth opinions on gender roles, the acceptability of violence, and awareness of VAW within their communities, aiming to uncover the underlying attitudes that perpetuate this issue. The analysis combines two methodological approaches to examine these data. A network-based approach explores relationships within item responses, allowing for an in-depth look at the direct interactions among youth attitudes. This approach is paired with a psychometric model based on Item Response Theory, specifically the Graded Response Model, which interprets attitudes as manifestations of latent traits, revealing how different factors shape perceptions of VAW. Together, these methods offer a comprehensive analysis of young people's views on VAW, highlighting both individual response patterns and broader cultural trends essential for designing effective interventions. Findings indicate a gradual shift in attitudes toward gender roles; however, traditional views remain prevalent, especially among young males. Socioeconomic factors, such as parents' employment status, also contribute to the persistence of stereotypes, underscoring the need for targeted interventions to address and reduce VAW in youth populations.
2603.28844Mar 2026
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A statistical perspective on higher-order interactions modeling
Modeling higher-order interactions (HOI) has emerged as a crucial challenge in complex systems analysis, as many phenomena cannot be fully captured by pairwise relationships alone. Hypergraphs, which generalize graphs by allowing interactions among more than two entities, provide a powerful framework for representing such intricate dependencies. Adopting a statistical and probabilistic perspective on hypergraph modeling, we propose a guided tour through this emerging research area. We begin by illustrating the ubiquity of HOI in real-world systems, where interactions often involve groups of entities rather than isolated pairs. We then introduce the foundational concepts and notations of hypergraphs, discussing their descriptive statistics, graph-based representations, and the challenges associated with their complexity. We further explore a variety of statistical models for hypergraphs and address the critical task of node clustering. We conclude by outlining some open challenges in the field.
2603.28273Mar 2026
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PyINLA: Fast Bayesian Inference for Latent Gaussian Models in Python
Bayesian inference often relies on Markov chain Monte Carlo (MCMC) methods, particularly required for non-Gaussian data families. When dealing with complex hierarchical models, the MCMC approach can be computationally demanding in workflows that require repeated model fitting or when working with models of large dimensions with limited hardware resources. The Integrated Nested Laplace Approximations (INLA) is a deterministic alternative for models with non-Gaussian data that belong to the class of latent Gaussian models (LGMs), yielding accurate approximations to posterior marginals in many applied settings. The INLA method was implemented in C as a standalone program, inla, that is widely used in R through the INLA package. This paper introduces PyINLA, a dedicated Python package that provides a Pythonic interface directly to the inla program. Therefore, PyINLA enables specifying LGMs, running INLA-based inference, and accessing posterior summaries directly from Python while leveraging the established INLA implementation. We describe the package design and illustrate its use on representative models, including generalized linear mixed models, time series forecasting, disease mapping, and geostatistical prediction, demonstrating how deterministic Bayesian inference can be performed in Python using INLA in a way that integrates naturally with common scientific computing workflows.
2603.27276Mar 2026
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Socioeconomic Drivers of Physical Morbidity Across U.S. Counties: A Spatial Causal Inference Approach
Identifying the causal effects of socioeconomic determinants on population health is of many great interests - from statistical methodology development to public health practitioners and policy developments. The statistical side of the problem needs to address several questions: spatial autocorrelation in both exposures and outcomes, confounding between treatments and covariates, and the need for geographically logical inference. We address these jointly by using spectral basis functions - Moran Eigenvector Maps and ICAR precision matrix eigenvectors - within a doubly robust generalized propensity score estimator for continuous treatments. Applied to 2022 county health data across the U.S. counties, the framework identifies the effect of six chosen predictors on the average physically unhealthy days per month. Possible further applications and methodological extensions are also discussed as future directions from this research.
2603.27095Mar 2026
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Forecastability as an Information-Theoretic Limit on Prediction
Forecasting is usually framed as a problem of model choice. This paper starts earlier, asking how much predictive information is available at each horizon. Under logarithmic loss, the answer is exact: the mutual information between the future observation and the declared information set equals the maximum achievable reduction in expected loss. This paper develops the consequences of that identity. Forecastability, defined as this mutual information evaluated across horizons, forms a profile whose shape reflects the dependence structure of the process and need not be monotone. Three structural properties are derived: compression of the information set can only reduce forecastability; the gap between the profile under a finite lag window and the full history gives an exact truncation error budget; and for processes with periodic dependence, the profile inherits the periodicity. Predictive loss decomposes into an irreducible component fixed by the information structure and an approximation component attributable to the method; their ratio defines the exploitation ratio, a normalised diagnostic for method adequacy. The exact equality is specific to log loss, but when forecastability is near zero, classical inequalities imply that no method under any loss can materially improve on the unconditional baseline. The framework provides a theoretical foundation for assessing, prior to any modelling, whether the declared information set contains sufficient predictive information at the horizon of interest.
2603.27074Mar 2026
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