RUI: Harmonious and Parsimonious Considerations for Correcting New Chemical and Instrumental Effects and Calibration Transfer

爱达荷州立大学的约翰·卡利瓦斯教授的这项研究得到了化学部的分析和表面化学项目以及NSF数学科学优先领域下的数学科学部的统计项目的支持。这是一项旨在改进光谱数据分析的统计研究。这些活动属于被称为“化学计量学”的专门领域。我们的想法是开发多变量校准，当预测值的数量超过样本数量(pn，而不是通常的假设np)时，这是有意义的。Kalivas教授提出了三种新的模型方法，解决了将一台仪器上开发的校准模型转移到其他仪器或为多台仪器建立校准模型的一般问题。这三个模型都基于Tikhonov正则化。每一种方法都使用差异和偏见来处理和谐和和谐/节俭平衡这两个问题。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。为了在这些领域取得进展，需要将最先进的统计方法整合到自然科学学科中。对本科生进行这些先进方法的教育，为他们从事任何类型的科学专业工作做好准备。

This study by Professor John Kalivas of Idaho State University is supported 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. This is a statistical study aimed at improving 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). Professor Kalivas proposes three new model approaches that address the general problem of the transfer of a calibration model developed on one instrument to other instruments, or the problem of building a calibration model for several instruments.All three models are based on Tikhonov Regularization. Each uses both variance and bias to address the two issues of harmony and harmony/parsimony balance. The work is being done by undergraduates using the program MATLAB.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.