421 papers
This paper proposes a deep learning framework utilizing multiplicative ensembling of neural networks (MLA-MoS-1 and MLA-MoS-2) to simultaneously solve the American double Heston model under both constant and stochastic interest rates, effectively predicting the early exercise boundary, option values, and Greeks.
This study proposes and evaluates a novel family of generalized sine-type and exponential-sine-type estimators for the finite population median under simple random sampling without replacement, demonstrating through theoretical analysis, simulations, and real-world data that these methods significantly outperform traditional estimators by leveraging auxiliary information to reduce mean squared error.
This paper introduces swNIsim, a flexible, simulation-based R package that enables power calculation for noninferiority stepped-wedge cluster randomized trials by accommodating diverse design features, outcome types, and analytical strategies while demonstrating through simulations that increasing time periods and balancing cluster allocation enhances statistical power.
This paper introduces Local Distributional Calibration, a non-parametric method that infers coherent full predictive distributions at unsampled locations from spatial ensembles to provide accurate exceedance probabilities for heterogeneous environmental fields, outperforming traditional kriging approaches by correcting under-dispersion and eliminating order-relation violations.
This tutorial presents a reproducible, code-supported workflow for conducting mediation analysis with clustered data in health psychology, demonstrated through a multi-wave study of China that links macro-level environmental factors to micro-level social participation and suicide-related vulnerability while addressing statistical challenges like clustering and multiple comparisons.
This study proposes and evaluates a statistical intelligence framework to measure digital transformation in the Kurdistan Region of Iraq's public sector, utilizing secondary data and various statistical methods to reveal a moderate overall performance score of 4.60/7, with high proficiency in digital identity capabilities but significant room for improvement in service accessibility.
This paper proposes a simple, computationally efficient collinearity test statistic for hypothesis testing in high-dimensional generalized linear models that remains effective when the number of predictors exceeds the sample size and demonstrates superior statistical power, particularly in sparse alternative scenarios, compared to classical Wald and likelihood-ratio tests.
This paper presents an extension of the open-source R package saemix to fit non-Gaussian non-linear mixed-effect models, demonstrating its flexibility and accuracy through diverse real-world applications and a simulation study on repeated binary data.
This study analytically and computationally determines the optimal periodic testing intervals for a single standby unit by modeling the trade-off between hidden fault exposure and offline service time, while quantifying how repeated detection uncertainty and the inclusion of periodic full tests impact overall system readiness.
This paper presents EduVision DV, a reproducible data pipeline and interactive Tableau dashboard system that integrates and harmonizes multiple university ranking datasets with World Bank education indicators to enable cross-system comparison while explicitly disclosing underlying data quality limitations such as estimated scores and constant values.