应用统计
Early outbreak data analysis is critical for informing about their potential impact and interventions. However, data obtained early in outbreaks are often sensitive and subject to strict privacy restrictions. Thus, federated analysis, which…
Extreme heat is an escalating public health concern. Although prior studies have examined heat-health associations, their reliance on restricted diagnoses and diagnostic categories misses or misclassifies heat-related illness. We conducted…
Recent developments in financial time series focus on modeling volatility across multiple assets or indices in a multivariate framework, accounting for potential interactions such as spillover effects. Furthermore, the increasing…
Weather conditions can drastically alter the state of crops and rangelands, and in turn, impact the incomes and food security of individuals worldwide. Satellite-based remote sensing offers an effective way to monitor vegetation and climate…
Debates about whether development projects improve living conditions persist, partly because observational estimates can be biased by incomplete adjustment and because reliable outcome data are scarce at the neighborhood level. We address…
Understanding historical datasets, such as the England and Wales infant mortality data, for local government districts can provide valuable insights into our changing society. Such analyses can prove challenging in practice, due to frequent…
This study develops an integrated, intersectional climate vulnerability assessment for Greensboro, North Carolina, a midsize city in the rapidly changing American Southeast. Moving beyond generalized mapping, we combine demographic,…
Macroeconomic conditions influence the environments in which health systems operate, yet their value as leading signals of health system capacity has not been systematically evaluated. In this study, we examine whether selected…
Current alignment-based methods for classification in geometric morphometrics do not generally address the classification of new individuals that were not part of the study sample. However, in the context of infant and child nutritional…
Reinforcement learning (RL) has achieved remarkable success in real-world decision-making across diverse domains, including gaming, robotics, online advertising, public health, and natural language processing. Despite these advances, a…
Ensemble forecasts have become a cornerstone of large-scale disease response, underpinning decision making at agencies such as the US Centers for Disease Control and Prevention (CDC). Their growing use reflects the goal of combining…
Species distribution models (SDMs) are widely used to assess the effects of environmental factors on species distributions. However, classical SDMs ignore inter-species dependencies. Multivariate SDMs (MSDMs), especially those based on…
This guide based on recent papers should help researchers avoid some of the most common pitfalls of missing value imputation imputation.
The U.S. Food and Drug Administration (FDA) released a landmark draft guidance in January 2026 on the use of Bayesian methodology to support primary inference in clinical trials of drugs and biological products. For sponsors, the central…
Against the backdrop of e-commerce restructuring consumption patterns, last-mile delivery stations have substantially fulfilled the function of community retail distribution. However, the current tax system only levies a low labor service…
Clinical AI systems frequently suffer performance decay post-deployment due to temporal data shifts, such as evolving populations, diagnostic coding updates (e.g., ICD-9 to ICD-10), and systemic shocks like the COVID-19 pandemic. Addressing…
Hospital readmission among patients with chronic heart failure (HF) is a major clinical and economic burden. Dynamic prediction models that leverage longitudinal biomarkers may improve risk stratification over traditional static models.…
This paper focuses on drawing information on underlying processes, which are not directly observed in the data. In particular, we work with data in which only the total count of units in a system at a given time point is observed, but the…
Entity resolution (probabilistic record linkage, deduplication) is a key step in scientific analysis and data science pipelines involving multiple data sources. The objective of entity resolution is to link records without common unique…
Compressed sensing, which involves the reconstruction of sparse signals from an under-determined linear system, has been recently used to solve problems in group testing. In a public health context, group testing aims to determine the…