252 papers
This paper introduces a Bayesian modeling framework that jointly addresses misclassification and selection errors in multi-source binary data, demonstrating through simulations and empirical analysis that while composite error can be robustly identified, accurately decomposing it into specific components requires valid indicators, auxiliary variables, and informative priors.
This paper proposes a general framework for quantifying the stability of non-deterministic prediction models across metric, categorical, and ordinal outcomes by introducing data-based and model-based stability measures that overcome the limitations of existing methods, such as their inability to handle individual test objects and their sensitivity to data composition.
This study develops a sequential tour-based mode choice framework for Sydney using GPS data and machine-learning-derived features, demonstrating that Mixed Logit models significantly improve prediction for interdependent tour components and that the framework remains robust to upstream estimation uncertainty, thereby validating the use of simulated inputs for practical travel demand forecasting.
This study demonstrates that on credit default prediction tasks, XGBoost's built-in `scale_pos_weight` parameter significantly outperforms the traditional SMOTE resampling technique in recall, highlighting that the optimal strategy for handling class imbalance depends on the specific classifier used rather than a universal resampling method.
This paper proposes a method for constructing estimated ranking tables with joint confidence regions for the true ranking of K populations, utilizing normality assumptions and standard errors to visually communicate ranking uncertainty and provide marginal confidence sets for individual ranks.
This paper proposes the Clinical Risk-Weighted Score (CRWS), a novel metric that decouples the penalties for false negatives and false positives to better reflect clinical priorities, demonstrating through MIMIC-IV analysis that it significantly alters model rankings and reveals greater clinical advantages for top-performing algorithms compared to the traditional F1 score.
This paper proposes a practical framework using summary statistics to screen for skewness and heteroscedasticity via the coefficient of variation and log-scale variance ratio, enabling researchers to recover valid, more powerful parametric inference for small-sample clinical data where traditional normality tests fail and standard t-tests are deficient.
The paper introduces T_root, a novel closed-form, parameter-free statistic that achieves accurate size calibration and robust power for testing independence in contingency tables across a wide range of sparsity and marginal heterogeneity, effectively unifying and outperforming existing methods like Pearson's chi-square and the Cressie-Read statistic while providing a deterministic solution without the need for permutation or bootstrap.
This paper introduces the Generalized Moment-Transmuted Cauchy (GMTC) distribution, a flexible heavy-tailed model that overcomes the classical Cauchy distribution's limitation of non-existent moments by incorporating a shape parameter to control tail behavior and ensure finite moments, while providing a comprehensive analysis of its properties, estimation methods, and practical applications.
This systematic review and meta-analysis argues that the observed lower prevalence of allergies in farming environments is likely an artifact of sampling bias and healthy worker effects rather than evidence of a genuine protective causal relationship.