Collaborative Research: Variable Selection for Remedying the Effects of Uncontrolled Variation in Data Driven Predictions

协作研究：用于补救数据驱动预测中不受控制的变化影响的变量选择

• 批准号: 1506853
• 负责人: Steven Brown
• 金额: $ 52万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-15 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1506853&HistoricalAwards=false
• 关键词: Collaborative; Research; Variable; Selection; Remedying

In a project funded by the Chemical Measurement and Imaging Program of the Chemistry Division, Professors Steven Brown and Karl Booksh, both from the University of Delaware, and Professor Barry Lavine of Oklahoma State University, seek to improve mathematical and statistical modeling of real-time measurement of chemical species. This research is aimed at reducing the cost-of-ownership of calibration and classification models that have become increasingly common in process monitoring, quality assurance and quality-by-design applications by studying the ways in which these models fail and by searching for ways to stabilize the modeling. The broader impacts of this project include the improvements in efficiency and performance from improved and more reliable chemical models for production and quality assurance, as well as the training of graduate and undergraduate students in data analysis, a skill in very high demand.This project is a collaborative effort aimed at investigating fundamental issues important to chemical modeling in modern measurement science: (1) stabilizing calibration and classification relationships in the presence of unexpected contributions, (2) making calibrations and classifications as robust as possible to additional sources of background, and (3) improving classification and calibration models measured over multiple instruments. Methods for automated identification of the portions of the chemical responses that best model the system are being investigated.

在化学部化学测量和成像计划资助的项目中，特拉华大学的史蒂文·布朗（Steven Brown）和卡尔（Karl Booksh）教授以及俄克拉荷马州立大学的Barry Lavine教授旨在改善化学物种实时测量的数学和统计模型。这项研究旨在减少校准和分类模型的拥有成本，这些模型在过程监测，质量保证和逐质设计应用程序中变得越来越普遍，通过研究这些模型失败的方式以及通过寻找稳定建模的方法来进行的。该项目的更广泛的影响包括改进和更可靠的生产和质量保证的化学模型的提高效率和绩效，以及对数据分析的研究生和本科生的培训，非常高需求的技能，该项目是一项协作性的努力，旨在研究对现代化的量化和稳定的化学成分的基本建模：（1（1）稳定的稳定性，（1（1）稳定相关性：（1）（1）（1）（1）（1）（1（1）（1）（1（1）（1）（1（1）（1）（1（1）（1（1）（1）（1（1）（1）（1（1）（1（1）（1（1）（1（1）（1）（1（1）（1（1）（1（1）（1）（1）（1）（1）（1）（1）（1）（1）（1）（1）（1）（1）（1）（1）（1）（1）（1）（1）（1）（1）（1（1）分类尽可能稳健地与背景的其他来源，以及（3）改进通过多种仪器测量的分类和校准模型。自动识别最佳模型的化学响应部​​分的自动鉴定方法正在研究中。