82 papers
This paper benchmarks Multivariate Imputation by Chained Equations (MICE) against deep generative models like Variational Autoencoders and Conditional Tabular GANs for synthetic ratemaking data, finding that MICE offers a simpler yet high-fidelity alternative that effectively preserves statistical distributions and supports robust Generalized Linear Model training.
This paper investigates how malicious auditees can construct fairness-compliant yet representative-looking samples from non-compliant distributions to deceive auditors, formalizes these manipulation strategies using optimal transport and entropic projections, and proposes statistical tests to detect such distributional manipulation attacks.
This paper proposes a neural process-based decision support system that forecasts patient vital sign trajectories to probabilistically predict readiness for switching from intravenous to oral antibiotics, thereby outperforming random selection and historical decision-learning approaches in identifying eligible patients across US and UK datasets.
This paper introduces SEED-SET, a Bayesian experimental design framework that combines hierarchical Gaussian Processes with stakeholder-specific subjective and objective evaluations to efficiently and effectively benchmark the ethical alignment of autonomous systems in high-stakes domains.
This paper proposes Singular Bayesian Neural Networks, which parameterize weights as low-rank matrices to induce a singular posterior that captures structured correlations, thereby achieving competitive predictive performance and improved uncertainty calibration with significantly fewer parameters and tighter generalization bounds compared to standard mean-field approaches.
This paper introduces EarthquakeNPP, a rigorous benchmarking platform that addresses data leakage and evaluation gaps in previous studies, revealing that current Neural Point Process models fail to outperform the classical ETAS model for earthquake forecasting.
This paper proposes a systematic framework for empirically developing evidence-based tailoring variables for adaptive interventions, arguing that while secondary data can be used, specifically designed optimization experiments (such as SMARTs or factorial designs) provide the most direct causal evidence for determining optimal measurement times, decision points, and cutoffs.
This paper proposes a novel data-driven framework using offline reinforcement learning and survival analysis to estimate optimal pricing and inventory control policies in sequential decision-making environments characterized by censored and dependent demand, overcoming challenges like missing profit information and non-stationarity through high-order Markov decision process approximations and finite-sample regret guarantees.
This paper proposes a framework for calibrating clinical trial designs to formally link statistical significance with clinical equipoise imbalance, demonstrating that standard power and error rates in phase 2 and 3 oncology studies provide robust evidence of equipoise imbalance when outcomes are consistent, while inconsistent results would require impractically large sample sizes to achieve the same level of evidence.
This paper proposes a practical Kernel Density Estimator-based method to quantify membership disclosure risk in tabular synthetic data by modeling nearest-neighbor distances, demonstrating through empirical evaluation that it outperforms existing baselines in accuracy and efficiency without requiring computationally expensive shadow models.
This paper proposes a novel summary-statistics-based joint analysis method that controls the joint local false discovery rate (Jlfdr), demonstrating through simulations and empirical data that it offers superior power over traditional meta-analysis methods, particularly when analyzing heterogeneous genome-wide association study datasets.
By applying a novel data-driven spectral factorization to JWST observations of Europa's leading hemisphere, this study reveals that CO2 enrichment extends beyond Tara Regio in a lens-like pattern across chaos units, suggesting that volatile retention is driven by near-surface microphysics and offering new insights into surface-interior exchange and the moon's habitability.
This paper proposes two probabilistic measures, Reproducibility Rate (RR) and False Irreproducibility Rate (FIR), to quantitatively evaluate the behavior of primary positive associations in replication studies, offering tools to guide study design and identify potentially true associations among irreproducible findings.
This paper proposes a Bayesian Synthetic Likelihood (BSL) framework integrated with Hamiltonian Monte Carlo to enhance Multilevel Network Meta-Regression by leveraging available subgroup-level summary statistics to impute missing individual-level covariates, thereby improving population-adjusted treatment comparisons in the presence of incomplete data.
This paper introduces a novel framework combining the expected Goal Action (xGA) metric with a Player's Restricted Shapley (PRS) statistic to quantify individual player contributions in football shot actions by modeling team interactions through tactically admissible passing coalitions, as demonstrated in an analysis of the 2022/23 Serie A season.
This paper demonstrates that under the ICH E9(R1) framework, the choice of a non-inferiority margin must be explicitly aligned with the specific estimand, as variations in intercurrent event strategies and historical trial designs significantly influence the estimated treatment effect and the validity of the constancy assumption.
Motivated by a gastric cancer trial, this paper proposes a Bayesian framework for precision clinical trials that utilizes individually weighted external data to partially borrow information for estimating subgroup effects, supported by a simulation study comparing its performance to dynamic borrowing and demonstrating its application in determining sample sizes and decision boundaries.
This paper introduces a set of Python tools designed to bridge the gap between the theoretical framework of Kendall's 3D shape space and practical computational workflows, addressing specific limitations in existing libraries like Geomstats to facilitate advanced 3D shape analysis.
This paper proposes a model-agnostic, distribution-free approach using split and sequential conformal prediction to construct prediction intervals for high-dimensional functional time series, demonstrating their effectiveness in forecasting subnational mortality rates for Japan and Canada compared to traditional methods.
This paper introduces Gimbal Regression, a deterministic and geometry-aware local regression framework that ensures stable, auditable estimation of spatial heterogeneity by constructing directional weights from neighborhood geometry to mitigate numerical instability caused by anisotropic or low-dimensional data structures.