arXiv subjects

Yuri Calleo

Publications and source records attributed to Yuri Calleo.

3 recordsLinked to original sources

arXiv2026

Calibration scoring under dependent seed variables in Cooke's Classical Model: a Mehler-Hermite design-effect correction

Cooke's Classical Model scores the statistical accuracy of experts by the $p$-value of a likelihood ratio test that compares the bin frequencies of realized seed variables with nominal probabilities, using a $χ^2$ reference. This calibration score drives performance-based weights and implicitly assumes independent seed variables, although seeds often share a common driver, such as the same indicator in several regions or periods. Under dependence the statistic converges to a weighted sum of $χ^2_1$ variables, and positive dependence makes the classical score anti-conservative. Under a Gaussian-copula working model, a Mehler-Hermite expansion gives the long-run covariance of the bin indicators in closed form, defines a design effect and an effective number of seed variables, and shows that the first-order correction coincides with the ad hoc calibration power of existing software. Rao-Scott-type and parametric-bootstrap corrected scores are proposed, together with a restricted maximum likelihood estimator of the nuisance dependence, computed from rank normal scores and invariant to location-scale miscalibration, and a prepivoted bootstrap that propagates its uncertainty. In simulations the classical score rejects up to 68\% of calibrated experts at a nominal 5\% level, whereas the corrected scores remove most of the excess rejection. In 45 published studies, design effects of 1.25-2 shift more than 5\% of the optimized weight in 29-44\% of studies. In three social-science studies with structured seeds, the baseline estimates imply that 18-21 seed variables carry the information of only 11-13 independent ones, although conclusions about individual experts depend on how the dependence is specified.

stat.ME↗

arXiv2026

Community detection in small-sample ordinal regimes: A benchmarking framework for Delphi data

The statistical modeling of consensus in Delphi data faces a critical bottleneck: the high dimensionality of questionnaire items relative to the limited sample size of expert panels. This rank deficiency leads traditional latent variable models, such as Principal Component Analysis, to be structurally unstable and prone to overfitting. Addressing this methodological gap, this study proposes a transition from variable-centric covariance models to network-centric connectivity models. By mapping item correlations onto a weighted graph topology, we present a simulation-based benchmark that utilizes community detection algorithms to identify latent thematic structures, effectively addressing the spectral instability and rank deficiency typical of high-dimensional, low-sample-size regimes. The research systematically evaluates the robustness of topological approaches based on structural density, information flow, and spectral partitioning against synthetic datasets designed to replicate the pathological conditions of consensus data, including ordinal scales and systemic noise. The central methodological contribution lies in demonstrating that collinearity among expert judgments - traditionally treated as statistical redundancy to be regularized - can be effectively reinterpreted as a topological signal of cohesion. This framework provides researchers with a structured and automated procedure for dimensionality reduction, ensuring structural stability and psychometric consistency even in small-sample regimes where standard factor analysis breaks down.

stat.ME↗

arXiv2025

Spatially-informed transformers: Injecting geostatistical covariance biases into self-attention for spatio-temporal forecasting

The modeling of high-dimensional spatio-temporal processes presents a fundamental dichotomy between the probabilistic rigor of classical geostatistics and the flexible, high-capacity representations of deep learning. While Gaussian processes offer theoretical consistency and exact uncertainty quantification, their prohibitive computational scaling renders them impractical for massive sensor networks. Conversely, modern transformer architectures excel at sequence modeling but inherently lack a geometric inductive bias, treating spatial sensors as permutation-invariant tokens without a native understanding of distance. In this work, we propose a spatially-informed transformer, a hybrid architecture that injects a geostatistical inductive bias directly into the self-attention mechanism via a learnable covariance kernel. By formally decomposing the attention structure into a stationary physical prior and a non-stationary data-driven residual, we impose a soft topological constraint that favors spatially proximal interactions while retaining the capacity to model complex dynamics. We demonstrate the phenomenon of ``Deep Variography'', where the network successfully recovers the true spatial decay parameters of the underlying process end-to-end via backpropagation. Extensive experiments on synthetic Gaussian random fields and real-world traffic benchmarks confirm that our method outperforms state-of-the-art graph neural networks. Furthermore, rigorous statistical validation confirms that the proposed method delivers not only superior predictive accuracy but also well-calibrated probabilistic forecasts, effectively bridging the gap between physics-aware modeling and data-driven learning.

cs.LG↗