Semiparametric approaches for mitigating spatial confounding in large environmental epidemiology cohort studies [0.03%]
用于大型环境流行病学队列研究的半参数方法以缓解空间混杂效应
Maddie J Rainey,Kayleigh P Keller
Maddie J Rainey
Epidemiological analyses of environmental risk factors often include spatially-varying exposures and outcomes. Unmeasured, spatially-varying factors can lead to confounding bias in estimates of associations with adverse health outcomes. Sev...
A hierarchical constrained density regression model for predicting cluster-level dose-response [0.03%]
分层约束密度回归模型预测群组剂量效应关系
Michael L Pennell,Matthew W Wheeler,Scott S Auerbach
Michael L Pennell
With the advent of new alternative methods for rapid toxicity screening of chemicals comes the need for new statistical methodologies which appropriately synthesize the large amount of data collected. For example, transcriptomic assays can ...
Penalized distributed lag interaction model: Air pollution, birth weight, and neighborhood vulnerability [0.03%]
带有惩罚项的分布滞后交互作用模型：空气污染、出生体重和社区脆弱性之间的关系研究
Danielle Demateis,Kayleigh P Keller,David Rojas-Rueda et al.
Danielle Demateis et al.
Maternal exposure to air pollution during pregnancy has a substantial public health impact. Epidemiological evidence supports an association between maternal exposure to air pollution and low birth weight. A popular method to estimate this ...
Assessing predictability of environmental time series with statistical and machine learning models [0.03%]
用统计和机器学习模型评估环境时间序列的可预测性
Matthew Bonas,Abhirup Datta,Christopher K Wikle et al.
Matthew Bonas et al.
The ever increasing popularity of machine learning methods in virtually all areas of science, engineering and beyond is poised to put established statistical modeling approaches into question. Environmental statistics is no exception, as po...
Marginal inference for hierarchical generalized linear mixed models with patterned covariance matrices using the Laplace approximation [0.03%]
Laplace近似在模式协方差矩阵的分层广义线性混合模型中的边缘推断
Jay M Ver Hoef,Eryn Blagg,Michael Dumelle et al.
Jay M Ver Hoef et al.
We develop hierarchical models and methods in a fully parametric approach to generalized linear mixed models for any patterned covariance matrix. The Laplace approximation is used to marginally estimate covariance parameters by integrating ...
Fast Grid Search and Bootstrap-based Inference for Continuous Two-phase Polynomial Regression Models [0.03%]
快速网格搜索和基于Bootstrap的推断在连续两阶段多项式回归模型中的应用
Hyunju Son,Youyi Fong
Hyunju Son
Two-phase polynomial regression models (Robison, 1964; Fuller, 1969; Gallant and Fuller, 1973; Zhan et al., 1996) are widely used in ecology, public health, and other applied fields to model nonlinear relationships. These models are charact...
An illustration of model agnostic explainability methods applied to environmental data [0.03%]
模型无关的可解释性方法在环境数据中的应用示例
Christopher K Wikle,Abhirup Datta,Bhava Vyasa Hari et al.
Christopher K Wikle et al.
Historically, two primary criticisms statisticians have of machine learning and deep neural models is their lack of uncertainty quantification and the inability to do inference (i.e., to explain what inputs are important). Explainable AI ha...
A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution [0.03%]
一种用于颗粒数浓度分布源解析的依赖型贝叶斯狄利克雷过程模型
Oliver Baerenbold,Melanie Meis,Israel Martínez-Hernández et al.
Oliver Baerenbold et al.
The relationship between particle exposure and health risks has been well established in recent years. Particulate matter (PM) is made up of different components coming from several sources, which might have different level of toxicity. Hen...
Two years of COVID-19 pandemic: The Italian experience of Statgroup-19 [0.03%]
新冠疫情两年的经验——Statgroup-19的意大利抗疫之路
Giovanna Jona Lasinio,Fabio Divino,Gianfranco Lovison et al.
Giovanna Jona Lasinio et al.
The amount and poor quality of available data and the need of appropriate modeling of the main epidemic indicators require specific skills. In this context, the statistician plays a key role in the process that leads to policy decisions, st...
Continuous Model Averaging for Benchmark Dose Analysis: Averaging Over Distributional Forms [0.03%]
基准剂量分析中的连续模型平均方法：在分布形式上的平均值
Matthew W Wheeler,Jose Cortinas,Marc Aerts et al.
Matthew W Wheeler et al.
When estimating a benchmark dose (BMD) from chemical toxicity experiments, model averaging is recommended by the National Institute for Occupational Safety and Health, World Health Organization and European Food Safety Authority. Though num...