85 papers
This study develops a comprehensive analytical framework combining ABESS-based variable selection, logistic regression, and Generalized Additive Models to accurately identify and interpret the clinical and behavioral factors driving hospitalization cost overruns for pulmonary infections under DRG payment reforms.
This paper demonstrates that aggregate Poisson predictive log-likelihood is a flawed metric for cross-city crime model comparison due to a scale confound favoring sparser populations, and proposes a superior evaluation framework using nonzero event-weighted likelihood and calibration ratios instead.
This paper presents an exact, differentiable Bayesian inference method for the Hurst index in fractional stochastic volatility models by integrating the Davies–Harte circulant-embedding algorithm into Hamiltonian Monte Carlo, thereby achieving computational efficiency and unbiased posterior estimates that refute the rough volatility hypothesis in favor of long-memory dynamics for the VN30 index, unlike biased Whittle approximations.
This paper presents a reproducible Bayesian multi-modal latent-state inference pipeline that successfully identifies a significant spaceflight signature in a small n=4 deep-space analogue dataset by fusing seven high-dimensional modalities, demonstrating that integrated multi-modal analysis outperforms individual biomarker detection in the p >> n regime.
This paper critiques the methodological limitations of the Clarivate Highly Cited Researchers list by demonstrating how factors like whole counting and citation disparities distort international comparisons, and proposes a reform framework to address these biases through fractional counting, field normalization, and improved author disambiguation.
This paper demonstrates that functional autoregressive models with exogenous variables (FARX(p)) significantly outperform traditional statistical and machine learning methods in forecasting day-ahead electricity prices in the Croatian market by effectively capturing high-frequency volatility, temporal dependencies, and demand-driven effects.
This study presents an interpretable forecasting framework using penalized logistic regression and bootstrap stability selection on historical team data to predict FIFA World Cup progression, identifying key predictors like market value and FIFA ranking while forecasting Argentina, France, Spain, England, Germany, and the Netherlands as top contenders for the 2026 tournament.
This paper unifies parameter estimation, hypothesis testing, and signal classification by demonstrating that all three tasks can be evaluated through a single functional—the captured fraction of a log-likelihood expansion in a fixed basis—which reveals their shared efficiency, termination conditions, and optimality across diverse distributions, including those where traditional orthogonal bases fail.
This study enhances the reliability of district-level teenage pregnancy estimates in Malawi by applying a Fay-Herriot small area estimation model to 2024 survey data and 2018 census auxiliary variables, thereby providing a more stable evidence base for decentralized policy and targeted interventions.
This paper proposes an iterative shrinkage weighted least squares algorithm based on penalized likelihood to estimate parameters for finite mixture models of mixed generalized estimating equations, addressing population heterogeneity where standard linear mixed models fail, and validates the method's performance through Monte Carlo simulations.