252 papers
This study introduces a bias-aware spatial statistical framework using 273,566 geotagged Sina Weibo posts to quantify and map the uneven digital visibility of Guangzhou's urban categories, revealing that while emotional visibility is scale-dependent, architectural visibility exhibits consistent spatial clustering across modern and historic districts.
This study introduces the ratio-metric BC-IRR effect size as a method to bridge single-case and group experimental designs, demonstrating through a reanalysis of technology-aided interventions for students with autism that BC-IRR offers distinct interpretive advantages over traditional metrics while enabling the synthesis of diverse evidence types.
The paper introduces SynthGuard-ReleaseBench, a reproducible audit framework that establishes pre-locked criteria for evaluating synthetic tabular data releases by comparing real and synthetic workflows to provide finite-sample bounds and distinct evidence for specific use cases, populations, and threat models rather than making blanket claims about privacy or safety.
This paper introduces the four-parameter Inverse Lomax Frechet (ILF) distribution by compounding the Frechet and Inverse Lomax Generalized families, derives its key statistical properties and maximum likelihood estimators, and demonstrates its superior fitting performance through applications to two real-life datasets.
This paper presents a data-driven modeling framework for tracking and forecasting COVID-19 dispersion using minimax measurements on occurrence position data, with a focus on model verification, validation, and robustness to improve predictive capabilities.
Distribird is an agentic web application that automates the creation of literature-informed prior distributions for Bayesian model calibration by deploying a multi-agent pipeline to extract, weight, and fit scientific data, thereby offering a traceable and validity-checked alternative to the commonly used uniform priors or ungrounded single-prompt LLM baselines.
This paper retrospectively validates a machine-learning model for New York City housing cost burdens, revealing systematic under-prediction due to static economic assumptions and subsequently recalibrating the framework with uncertainty-aware methods to generate more robust, scenario-based forecasts for 2025–2028.
This paper proposes a new matrix variate skew-normal distribution and its finite mixture model to effectively capture asymmetry and perform clustering in matrix-valued data, offering improved flexibility and performance over traditional symmetric models through derived estimators and an ECM algorithm.
This paper proposes a Seemingly Unrelated Autoregressive Distributed Lag (SUR-ARDL) framework for small-N panels that enhances estimation efficiency and cointegration testing power by leveraging cross-equation error correlations while avoiding the finite-sample distortions of long-run covariance matrix estimators, as demonstrated through theoretical proofs, Monte Carlo simulations, and an application to the renewable energy-growth nexus in ASEAN countries.
This paper proposes a closed-loop, human-AI collaborative framework that iteratively refines knowledge-based causal models (DAGs) by integrating process mining of empirical event logs with expert adjudication to enhance the accuracy and transparency of causal inference in complex chronic diseases.