Multivariate calibration on heterogeneous samples
Document Type
Article
Publication Date
10-15-2021
Abstract
Data heterogeneity has become a challenging problem in modern data analysis. Classic statistical modeling methods, which assume the data are independent and identically distributed, often show unsatisfactory performance on heterogeneous data. This work is motivated by a multivariate calibration problem from a soil characterization study, where the samples were collected from five different locations. Newly proposed and existing signal regression models are applied to the multivariate calibration problem, where the models are adapted to handle such spatially clustered structure. When compared to a variety of other methods, e.g. kernel ridge regression, random forests, and partial least squares, we find that our newly proposed varying-coefficient signal regression model is highly competitive, often out-performing the other methods, in terms of external prediction error.
Publication Source (Journal or Book title)
Chemometrics and Intelligent Laboratory Systems
Recommended Citation
Li, B., Marx, B., Chakraborty, S., & Weindorf, D. (2021). Multivariate calibration on heterogeneous samples. Chemometrics and Intelligent Laboratory Systems, 217 https://doi.org/10.1016/j.chemolab.2021.104386