Neutral-data Comparisons for Massive Multiple Testing in the Social Sciences

社会科学中大规模多重测试的中性数据比较

• 批准号: 1260803
• 负责人: Dan Spitzner
• 金额: $ 12万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-15 至 2015-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1260803&HistoricalAwards=false
• 关键词: Neutral; data; Comparisons; Massive; Multiple

This project contributes to ongoing efforts to understand the Bayesian approach to data-analysis, which since at least the 1990s has continued a profound expansion into mainstream statistical practice. The central concept is a novel assessment of evidence in Bayesian hypothesis testing and model-choice procedures, called a "neutral-data comparison," whose mechanisms dampen sensitivity to the choice of prior distribution and consequently expand the possibilities for powerful use of subjective information, especially vague subjective information, in statistical analysis. The objective of the project is to develop neutral-data comparisons into a comprehensive analysis tool. To this end, the project will develop theory and practical-minded neutral-data comparisons methodology for such model-choice problems as variable selection, model-based clustering, and nonparametric clustering. Special strategies for massive multiple-testing will be developed, to which neutral-data comparisons contribute a novel framework for eliciting dependencies within the discrete portion of the prior distribution. The project is guided by an applied interest in developing Bayesian methodology for life-course analysis in the social sciences, paralleling the established "optimal matching" technique for clustering life-course trajectories. The Bayesian approach to statistics identifies a critical role of subjective information in quantitative analysis. The value of Bayesian results for substantive questions has been noted by researchers in the social sciences. Neutral-data comparisons are valuable in this context because their underlying theory interprets subjective information in a way that expands the possibilities for its meaningful and efficacious use in statistical hypothesis testing. They have furthermore been shown to exhibit exceptionally strong performance in problems that involve massive numbers of hypothesis tests, which arise commonly and with increasing frequency in today?s information-rich, analytically sophisticated era. The project will contribute new, powerful techniques to a number of analysis methodologies that are widely used in the social sciences, as well as to specialized methodologies that aim to understand variability in sequential information obtained from life histories.

该项目有助于不断努力了解贝叶斯数据分析方法，至少自1990年代以来，贝叶斯方法一直在深入扩展到主流统计实践。 中心概念是一种新的评估证据的贝叶斯假设检验和模型选择程序，称为“中性数据比较”，其机制抑制敏感性的先验分布的选择，从而扩大了强大的主观信息，特别是模糊的主观信息，在统计分析的可能性。 该项目的目标是将中性数据比较发展成为一个综合分析工具。 为此，该项目将为变量选择、基于模型的聚类和非参数聚类等模型选择问题开发理论和实用的中立数据比较方法。 大规模的多重检验的特殊策略将被开发，其中中性数据的比较有助于一个新的框架内引起的先验分布的离散部分的依赖关系。 该项目的指导是在社会科学中发展贝叶斯方法学进行生命过程分析的应用兴趣，并行建立“最佳匹配”技术聚类生命过程轨迹。贝叶斯统计方法确定了主观信息在定量分析中的关键作用。 社会科学的研究人员已经注意到贝叶斯结果对实质性问题的价值。 在这种情况下，中性数据比较是有价值的，因为它们的基本理论解释了主观信息，扩大了其在统计假设检验中有意义和有效使用的可能性。 此外，他们已被证明表现出异常强大的性能，涉及大量的假设检验的问题，这是常见的，并在今天越来越频繁？的信息丰富，分析复杂的时代。 该项目将为社会科学中广泛使用的一些分析方法以及旨在了解从生活史中获得的连续信息的可变性的专门方法提供新的强大技术。