Computer-intensive Inference with Applications to Social Sciences

计算机密集型推理及其在社会科学中的应用

• 批准号: 1949845
• 负责人: Joseph Romano
• 金额: $ 29万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1949845&HistoricalAwards=false
• 关键词: Computer; intensive; Inference; Applications; Social

This research project will develop new statistical computer-intensive procedures that address current problems in the social sciences. Two different research questions that are related by common principles and methodologies will be addressed by this research. First, the project will develop new methods for generating inferences from ranked data that do not rely on strong model assumptions. It is common for data available from different populations (such as countries, schools, or hospitals) to be ordered by some performance measure, say from best to worst in the form of ranks. Improved methods for ranked data will have many applications in the social sciences, including the analysis of countries ranked in reading, math, and science or the ranking of neighborhoods by intergenerational income mobility. Second, the project will address the 'hot hand fallacy' and the human misperception of randomness. The common understanding of streaks in small samples has been challenged recently, and this research will provide solid statistical tools to address this controversy. In addition, graduate students will participate in the research process, and the project will develop free and easily accessible software.The problems to be addressed in this project are related by common inferential methodologies that will provide sound principles to areas in need of formal statistical analysis. So that inferential procedures will not rely on unverifiable model-based assumptions, computer-intensive methods of statistical inference will be used, such as resampling, bootstrap, and randomization methods. Although machine calculations will be developed, they will be accompanied by mathematical or theoretical results that justify their use. The problems to be addressed will require novel insights in order to develop rigorous statistical properties so that the methods may be applied safely in practice. In addition, the project will draw heavily on the literature in multiple testing and simultaneous inference. For example, inference for ranks will result in the construction of simultaneous confidence regions for ranks with guaranteed error control. This will allow researchers to know whether empirical rankings represent real differences between populations or whether they are just artifacts of the data. While the problems to be addressed stem from specific applied questions, they will require solutions that are of fundamental importance in statistics. These open problems are exciting and challenging not only from the point of view of mathematical statistics, but also because burgeoning applications demand new statistical methodology.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项研究项目将开发新的统计计算机密集型程序，以解决当前社会科学中的问题。这项研究将解决两个由共同原则和方法联系在一起的不同研究问题。首先，该项目将开发新的方法，从不依赖于强有力的模型假设的排名数据中生成推论。从不同人群(如国家、学校或医院)获得的数据通常是按某种绩效衡量标准排序的，比如按照排名的形式从最好到最差。改进的排名数据方法将在社会科学中有许多应用，包括对阅读、数学和科学排名的国家进行分析，或根据代际收入流动性对社区进行排名。其次，该项目将解决“热手谬误”和人类对随机性的误解。对小样本中条纹的普遍理解最近受到了挑战，这项研究将提供可靠的统计工具来解决这一争议。此外，研究生将参与研究过程，该项目将开发免费且易于使用的软件。该项目中要解决的问题通过共同的推理方法联系在一起，这些方法将为需要正式统计分析的领域提供可靠的原则。为了使推理过程不依赖于不可验证的基于模型的假设，将使用计算机密集的统计推断方法，如重抽样、自举和随机化方法。尽管将开发机器计算，但它们将伴随着证明其使用的数学或理论结果。要解决的问题将需要有新的见解，以便发展严格的统计性质，以便这些方法可以安全地应用于实践。此外，该项目将大量借鉴多重测试和同时推理方面的文献。例如，对于等级的推断将导致为具有保证差错控制的等级构建同时置信域。这将使研究人员知道经验性排名是否代表了人群之间的真正差异，或者它们是否只是数据的产物。虽然要解决的问题源于具体的应用问题，但它们需要在统计学上具有根本重要性的解决办法。这些悬而未决的问题不仅从数理统计的角度来看是令人兴奋和具有挑战性的，而且还因为新兴的应用程序需要新的统计方法。这个奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

The Romano-Wolf multiple-testing correction in Stata

Stata 中的 Romano-Wolf 多重检验校正

• DOI: 10.1177/1536867x209
• 发表时间: 2021
• 期刊: The Stata journal
• 作者: Clarke, D;Romano, J;Wolf, M.
• 通讯作者: Wolf, M.

Confidence intervals for seroprevalence

血清阳性率的置信区间

• DOI: 10.1093/restud/rdab020
• 发表时间: 2022
• 期刊: Statistical science
• 影响因子: 5.7
• 作者: DiCiccio, T;Ritzwoller, D;Romano, J;Shaikh, A.
• 通讯作者: Shaikh, A.

CLT for U-statistics with growing dimension

用于维度不断增长的 U 统计的 CLT

• DOI: 10.5705/ss.202020.0048
• 发表时间: 2021
• 期刊: Statistica sinica
• 影响因子: 1.4
• 作者: DiCiccio, C;Romano, J.
• 通讯作者: Romano, J.

Permutation testing for dependence in time series

• DOI: 10.1111/jtsa.12638
• 发表时间: 2020-09
• 期刊: Journal of Time Series Analysis
• 影响因子: 0.9
• 作者: Joseph P. Romano;Marius A. Tirlea