252 papers
This study develops and validates a Stacking-based ensemble machine learning model using 5.7 million viral load records from Uganda to accurately predict the next visit viral load for ART clients, achieving superior performance with an R² of 0.85 and utilizing LIME for model interpretability.
This paper introduces a model-independent workflow to distinguish genuine local scale dependence from artificial trends caused by regional mixing in urban block-size distributions, demonstrating through a comprehensive analysis of Tokyo that while regional heterogeneity flattens the distribution tail, smaller blocks exhibit significant local steepening that cannot be explained by compositional effects alone.
This paper proposes and validates two types of uncertain single-index models using semiparametric least-squares estimation with kernel and B-spline methods to effectively handle complex, imprecise data, as demonstrated through simulation studies and a practical weather application.
This paper presents the theoretical development and structure of the Practitioner Allegiance Risk Scale (PARS-6), a new six-item instrument designed to systematically screen and document investigator allegiance-related characteristics during data extraction in systematic reviews of intervention research, while explicitly clarifying that it serves as a descriptive risk indicator rather than a definitive measure of bias.
This study utilizes SciVal bibliometric data and forecasting models to reveal that while Philippine State Universities have significantly increased publication volumes from 2015 to 2025, substantial and persistent regional disparities in research output and impact—led by Region III—will continue through 2030 without targeted capacity-building investments rather than uniform incentives.
This paper proposes a statistical framework to identify admissible allocation weights that achieve Pareto improvements without harming any participant, demonstrating its practical utility through an empirical analysis of resource reallocation in the RAND Health Insurance Experiment.
This paper proposes a conditional sign randomization method with estimated whitening for Gaussian kinship models that enables computationally efficient, finite-sample level-controlled gene-set association testing by leveraging sign-symmetry and reusable calibration across a sign orbit, while explicitly distinguishing its global null inference target from competitive enrichment or causal gene discovery.
This paper introduces Laplacian-P-splines (LPS) as a computationally efficient, sampling-free Bayesian alternative for estimating shared Gamma frailty models in clustered right-censored time-to-event data, demonstrating its performance through simulations and applications to three biomedical datasets.
This paper proposes and evaluates two enhanced multivariate generalized likelihood ratio (MGLR) control charts that integrate Klein's 2-of-2 and 2-of-3 sensitizing rules to effectively monitor simultaneous shifts in process mean and dispersion while explicitly accounting for the detrimental impact of measurement error.
This paper introduces a structural equation formulation for general quasi-periodic Gaussian processes that simplifies generation and forecasting while reducing computational complexity from to , offering a scalable and consistent approach for analyzing diverse natural and physiological signals.