RUI: Multivariate Calibration as a Harmonious and Parsimonious Problem

RUI：多元校准是一个和谐且简约的问题

• 批准号: 0400034
• 负责人: John Kalivas
• 金额: $ 11.88万
• 依托单位: Idaho State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-01 至 2007-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0400034&HistoricalAwards=false
• 关键词: RUI; Multivariate; Calibration; Harmonious; Parsimonious

Professor John Kalivas of Idaho State University is funded for a statistical study aimed at improving, for example, quantitative structure activity relationships (QSAR) calculations, and analysis of spectroscopic data. These activities fall in the specialized field called "chemometrics." The idea is to develop multivariate calibration, which is of interest when the number of predictors exceeds the number of samples (pn, rather than the usual assumption np). The PI is using the regression vector LASSO (least absolute shrinkage and selection operator) and other norms (with constraints) to fit such data. Comparisons to other methods such as ridge regression, partial least squares and multiple linear regression, are being performed. The work is being done by undergraduate students using the program MATLAB. Collaboration with Dr. Karoly Heberger of the Central Research Institute for Chemistry of the Hungarian Academy of Sciences will extend the QSAR studies.This award is co-funded by the Analytical and Surface Chemistry program of the Chemistry Division and the Statistics program of the Division of Mathematical Sciences under the umbrella of the NSF-wide Mathematical Sciences Priority Area. Efficiency and effectiveness of data analysis is becoming increasingly important in all of the quantitative sciences, including biology. The integration of state of the art statistical methods within the natural science disciplines is required for advancement in these fields. Education of undergraduates in these advanced methods prepare them for any type of scientific professional pursuit.

爱达荷州州立大学的John Kalivas教授获得了一项统计研究的资助，该研究旨在改进定量结构活性关系（QSAR）计算和光谱数据分析等。 这些活动属于称为“化学计量学”的专门领域。“我们的想法是开发多变量校准，当预测因子的数量超过样本的数量（pn，而不是通常的假设np）时，这是有趣的。 PI使用回归向量LASSO（最小绝对收缩和选择算子）和其他范数（具有约束）来拟合这些数据。 与其他方法，如岭回归，偏最小二乘法和多元线性回归，正在进行比较。 这项工作是由本科生使用MATLAB程序完成的。 与匈牙利科学院化学中央研究所的Karoly Heberger博士合作将扩展QSAR研究。该奖项由化学部的分析和表面化学计划以及数学科学部的统计计划共同资助，该计划属于NSF范围内的数学科学优先领域。数据分析的效率和有效性在包括生物学在内的所有定量科学中变得越来越重要。 在自然科学学科的最先进的统计方法的整合是需要在这些领域的进步。 这些先进方法的本科生教育为他们从事任何类型的科学专业做好了准备。