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

在一个由化学部化学测量和成像项目资助的项目中，来自特拉华州大学的Steven Brown和Karl Booksh教授以及州立大学俄克拉荷马州巴里拉文教授寻求改进化学物种实时测量的数学和统计建模。这项研究的目的是通过研究这些模型失败的方式和寻找稳定建模的方法来降低校准和分类模型的拥有成本，这些模型在过程监控，质量保证和质量设计应用中变得越来越普遍。该项目的广泛影响包括通过改进和更可靠的化学模型来提高生产和质量保证的效率和性能，以及对研究生和本科生进行数据分析的培训，这是一项非常高的技能。该项目是一项合作努力，旨在调查现代测量科学中化学建模的重要基础问题：（1）在存在意外贡献的情况下稳定校准和分类关系，（2）使校准和分类对额外背景源尽可能稳健，以及（3）改进在多个仪器上测量的分类和校准模型。用于自动识别最佳模拟系统的化学反应部分的方法正在研究中。

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.