252 papers
This paper proposes a generalized Value of Information (IP-VOI) framework that extends conventional analysis to settings with imprecise probabilities by evaluating decision stability across an admissible set of probability measures, thereby distinguishing robust research conclusions from those dependent on specific modeling assumptions.
This study utilizes a CatBoost classifier on 48,130 workers' compensation claims to model attorney involvement as a probabilistic risk process, demonstrating how litigation drivers shift across low-, medium-, and high-risk tiers and revealing asymmetric feature patterns near regime boundaries to enhance risk stratification and explainability.
This paper presents a reproducible, field-agnostic pipeline that improves the accuracy of author location and seniority assignment in radiation oncology by deriving data directly from raw affiliation strings rather than relying on error-prone institutional tags, achieving significantly higher precision (F1 ≈ 0.92) than standard baselines.
This study developed and validated a reliable 19-item, three-factor scale to effectively assess women's knowledge, attitudes, acceptance, and trust regarding AI-based chatbot systems in the context of preconception counseling.
This study quantifies the significantly higher burden of name collisions faced by East-Asian researchers compared to their Anglophone and Other counterparts within the OpenAlex database, demonstrating that name-based attribution is inherently unequal and advocating for the adoption of identifier-based solutions like SigmaCV to ensure fair research assessment.
This paper proposes Bergsma-Dassios's τ∗ as a robust, sign-covariance-based dependence measure for official statistics, demonstrating through theoretical analysis, EU-SILC data applications, and simulation studies that it outperforms traditional correlation metrics in detecting nonlinear relationships and handling data contamination.
This paper introduces Quantile Necessary Condition Analysis (QNCA), a tolerance-parameterized extension of NCA that replaces the deterministic ceiling with conditional-quantile frontiers to mitigate the sensitivity of effect sizes to single boundary observations while preserving the method's core interpretive framework.
This paper introduces a reproducible, hybrid SIRD framework that estimates time-varying epidemic parameters via algebraic inversion and Vector Autoregression to outperform static and naive forecasting baselines in predicting COVID-19 cases and deaths, though it currently exhibits limited probabilistic calibration.
This paper introduces a machine learning approach using an adapted Partial Logistic Artificial Neural Network (PLANN) to estimate and predict net survival, demonstrating its flexibility in handling complex data structures compared to traditional spline-based regressions while noting higher variance in estimates for smaller sample sizes and providing the `survivalPLANN` R package for implementation.
This study introduces a graphon-based framework to analyze the structural evolution of Hungary's higher education mobility networks from 2006 to 2024, demonstrating that spectral and distributional distance metrics outperform traditional statistics in capturing policy-induced shifts and revealing hierarchical network tiers.