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 (p>n, rather than the usual assumption n>>p). 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.
