Model checks for Bayesian estimation and forecasting of health coverage indicators in low- and middle-income countries [0.03%]
用于低收入和中等收入国家卫生覆盖面指标的贝叶斯估计和预测的模型检验
Leontine Alkema,Shauna Mooney,Sophia Kagoye et al.
Leontine Alkema et al.
Statistical models are needed to produce estimates and forecasts of health coverage indicators in low- and middle-income countries, where data are often sparse and of uneven quality. We consider a class of Bayesian transition models for thi...
An automatic finite-sample robustness metric: when can dropping a little data change conclusions? Part I: definitions and experiments [0.03%]
一种自动的有限样本稳健性度量：剔除少量数据能否改变结论？第一部分：定义与实验
Ryan Giordano,Rachael Meager,Tamara Broderick
Ryan Giordano
Study samples often differ in non-random ways from the target populations to which policy decisions will eventually be applied. Researchers typically hope that such departures from random sampling-due to changes in the population over time ...
An automatic finite-sample robustness metric: when can dropping a little data change conclusions? Part II: theory and intuition [0.03%]
一种自动的有限样本稳健性度量：去掉一点数据是否可以改变结论？第二部分：理论与直观理解
Ryan Giordano,Rachael Meager,Tamara Broderick
Ryan Giordano
In Part I, we propose a method to assess the sensitivity of applied conclusions to the removal of a small fraction of the sample; we call our metric the approximate maximum influence perturbation (AMIP). In this article, we support the intu...
A preliminary data analysis workflow for meta-analysis of dependent effect sizes [0.03%]
有关依赖效应大小的元分析初步数据分析流程
James E Pustejovsky,Jingru Zhang,Elizabeth Tipton
James E Pustejovsky
In many fields of application, meta-analyses routinely involve dependent effect size estimates and hierarchical data structures. Statistical methods for analysing dependent effect sizes are now well-developed, but there has been less attent...
Predictability-Computability-Stability workflow for veridical data science in the age of artificial intelligence [0.03%]
人工智能时代可证数据科学的预测性-计算性-稳定性工作流
Zachary T Rewolinski,Bin Yu
Zachary T Rewolinski
Data science is a pillar of artificial intelligence (AI), which is transforming nearly every domain of human activity, from the social and physical sciences to engineering and medicine. While data-driven findings in AI offer unprecedented p...
Unsupervised machine learning for scientific discovery: workflow and best practices [0.03%]
无监督机器学习在科学发现中的工作流程及最佳实践
Andersen Chang,Tiffany Tang,Tarek Zikry et al.
Andersen Chang et al.
Unsupervised machine learning is widely used to mine large, unlabelled datasets to make data-driven discoveries in critical domains such as climate science, biomedicine, astronomy, chemistry and more. However, despite its widespread utiliza...
Lauren N Girouard,Susan A Gelman
Lauren N Girouard
In the study of children's thinking, the research process includes not just the visible steps of study design, data collection, data analyses and write-up but also hidden yet crucial steps that have consequences throughout the workflow proc...
Closing the gap between statistical and scientific workflows for improved forecasts in ecology [0.03%]
缩小统计工作流程和科学工作流程以改善生态预测之间的差距
Victor Van der Meersch,James Regetz,T Jonathan Davies et al.
Victor Van der Meersch et al.
Concerns about increasing biodiversity loss and climate change have led to greater demands for useful ecological models. Datasets relevant for developing these models have also increased in size and complexity, including in their geographic...
Machine learning workflows in climate modelling: design patterns and insights from case studies [0.03%]
机器学习在气候建模中的工作流程：设计模式及案例研究的见解
Tian Zheng,Subashree Venkatasubramanian,Shuolin Li et al.
Tian Zheng et al.
Machine learning (ML) has been increasingly applied in climate modelling on system emulation acceleration, data-driven parameter inference, forecasting and knowledge discovery, addressing challenges such as physical consistency, multi-scale...
Reproducible workflow for online artificial intelligence in digital health [0.03%]
数字化医疗中在线人工智能的可重复工作流程
Susobhan Ghosh,Bhanu T Gullapalli,Daiqi Gao et al.
Susobhan Ghosh et al.
Online artificial intelligence (AI) algorithms are an important component of digital health interventions. These online algorithms are designed to continually learn and improve their performance as streaming data are collected on individual...