252 papers
This paper introduces METER, a simulation-trained psychometric model that successfully infers person traits across diverse unseen instruments and populations without re-fitting, achieving high correlation with specialist estimates in real-world data while demonstrating limitations in handling specific structural complexities and establishing absolute validity.
This paper develops a variance-calibrated adjusted blockwise empirical likelihood method for comparing the means of two independent weakly dependent time series, demonstrating that it effectively corrects for variance mismatches and convex-hull restrictions to restore nominal coverage, particularly when using a fixed number of increasingly long blocks where the statistic follows a non-Wilks Gaussian reference law.
This paper proposes a zone-free probabilistic seismic hazard assessment framework for Sumatra that integrates a Log-Gaussian Cox process with tectonic covariates and an ETAS aftershock model, demonstrating that this approach yields significantly higher design accelerations than current Indonesian codes by eliminating arbitrary source zone boundaries.
The paper introduces MN2, a unified decision rule for multi-null multinomial testing that leverages Jensen-Shannon geometry to achieve exact p-value computation, finite-sample Type-I error control, and superior power in sparse regimes compared to standard independent testing with Holm correction.
This paper argues that current statistical software defaults for contingency tables are fundamentally flawed due to poor calibration in sparse or heterogeneous data, and proposes replacing them with a new, single closed-form statistic called T_root that offers superior size control and power without requiring resampling methods.
This paper introduces a Rao-Blackwellization-based variance-reduction framework for event-based Monte Carlo probabilistic seismic hazard analysis that eliminates unnecessary ground-motion residual sampling to produce more stable and efficient hazard estimates while unifying the method with classical formulations and extending it to adaptive importance sampling.
This simulation study demonstrates that the Relative Belief Ratio (RBR) filter generally outperforms other feature selection methods in recovering informative binary features and maintaining selection stability when sample sizes are at least 100 and marginal location signals are clear, though its performance declines under heavy-tailed or bimodal distributions where methods like Information Gain or Boruta are more effective.
This paper introduces a selective estimator for sparse circular orientation data that certifies winding residues by enforcing strict smoothness, magnitude, and branch margin criteria, thereby prioritizing the validation of arithmetic consistency over sampling accuracy and frequently abstaining from reporting results when these conditions are not met.
This paper proposes a graph-based audit protocol that distinguishes between level shifts and shape changes to ensure the administrative validity and compatibility of longitudinal datasets joined across different institutional regimes, demonstrating its efficacy through an analysis of Tunisia's economic transition from 2000 to 2019.
This paper conducts a sensitivity audit comparing national-IQ and harmonized-learning indicators, finding high correlations and similar rank distributions but concluding that the datasets are not interchangeable or causally equivalent despite their statistical similarities.