LEAPS-MPS: Getting modeling precision right for data with measurement errors

LEAPS-MPS：为具有测量误差的数据提供正确的建模精度

• 批准号: 2349860
• 负责人: Pei Geng
• 金额: $ 25万
• 依托单位: University of New Hampshire
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2349860&HistoricalAwards=false
• 关键词: LEAPS; MPS; Getting; modeling; precision

Data are often collected with measurement errors in many areas such as household income from economic surveys, self-reported daily fiber intake, and the exposure dose to radiation in epidemiological studies. This project aims to develop efficient and robust statistical tools for various model settings involving measurement errors. The research will improve the estimation accuracy in regression models and fill the void in model-checking procedures for the time series analysis. The application of the proposed methods will advance the understanding of precise relationships in a wide variety of fields such as the relationship between dietary fiber intake and the gut microbiome in epidemiology. The project will also promote research engagement of both undergraduate and graduate students, especially students from underrepresented groups, in mathematics and statistics.In statistical modeling, ignoring the measurement error in predictors often causes estimation bias and lower hypothesis testing power. This project will develop efficient estimation and powerful testing procedures with measurement error in a broad class of models including linear and nonlinear models, generalized linear models in case-control studies, and autoregressive models for time series data. The proposed estimation and model-checking methods are rooted in the robustness of the minimum distance estimation based on the weighted empirical residual processes, the efficiency of the nonparametric estimation techniques, and the power of information extraction from external validation studies on predictors. This research will not only establish the theory of the proposed methodologies but also apply these statistical tools to real-world datasets to help practitioners understand the underlying relationships more accurately and draw more precise conclusions.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.

在许多领域收集的数据往往存在测量误差，例如经济调查中的家庭收入、自我报告的每日纤维摄入量以及流行病学研究中的辐射照射剂量。该项目旨在为涉及测量误差的各种模型设置开发高效和强大的统计工具。该研究将提高回归模型的估计精度，填补时间序列分析中模型检验程序的空白。所提出的方法的应用将促进对各种领域中精确关系的理解，例如流行病学中膳食纤维摄入量与肠道微生物组之间的关系。该项目还将促进本科生和研究生，特别是来自代表性不足群体的学生在数学和统计学方面的研究参与。在统计建模中，忽略预测因子中的测量误差往往会导致估计偏差和假设检验功效较低。该项目将开发有效的估计和强大的测试程序，在广泛的模型，包括线性和非线性模型，病例对照研究中的广义线性模型，以及时间序列数据的自回归模型的测量误差。所提出的估计和模型检验方法是植根于最小距离估计的加权经验残差过程的基础上，非参数估计技术的效率，以及从外部验证研究预测的信息提取的权力的鲁棒性。这项研究不仅将建立拟议方法的理论，而且将这些统计工具应用于现实世界的数据集，以帮助从业者更准确地理解潜在的关系，并得出更准确的结论。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。