High-dimensional multivariate geostatistics: A Bayesian matrix-normal approach [0.03%]
高维多元地质统计学：一种贝叶斯矩阵正态方法
Lu Zhang,Sudipto Banerjee,Andrew O Finley
Lu Zhang
Joint modeling of spatially oriented dependent variables is commonplace in the environmental sciences, where scientists seek to estimate the relationships among a set of environmental outcomes accounting for dependence among these outcomes ...
Bayesian Inference for Spatially-Temporally Misaligned Data Using Predictive Stacking [0.03%]
基于预测堆叠的空间时间错配数据的贝叶斯推断
Soumyakanti Pan,Sudipto Banerjee
Soumyakanti Pan
Air pollution remains a major environmental risk factor that is often associated with adverse health outcomes. However, quantifying and evaluating its effects on human health is challenging due to the complex nature of exposure data. Recent...
Spike and Slab Regression for Nonstationary Gaussian Linear Mixed Effects Modeling of Rapid Disease Progression [0.03%]
用于疾病快速进展的非平稳高斯线性混合效应建模的脉冲和板条回归方法
Emrah Gecili,Cole Brokamp,Özgür Asar et al.
Emrah Gecili et al.
Select measures of social and environmental determinants of health (referred to as "geomarkers"), predict rapid lung function decline in cystic fibrosis (CF), defined as a prolonged decline relative to patient and/or center-level norms. The...
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...