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Multivariate variographic versus bilinear data modeling
Author(s) -
Minkkinen Pentti,
Esbensen Kim Harry
Publication year - 2014
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2514
Subject(s) - multivariate statistics , bilinear interpolation , chemometrics , statistics , variogram , mathematics , principal component analysis , computer science , data mining , econometrics , kriging , machine learning
Two contrasting multivariate data sets (a process data series vs. a 1‐D geochemical soil profile) are analyzed to illustrate the benefits of using bilinear projection scores for variographic characterization instead of using individual variables. By using absolute variograms on a validated number of component scores, it is possible to make a combined multivariate chemometrics–variogram characterization of heterogeneous processes and materials as well as 1‐D transects, no longer restricted to a one‐variable‐at‐a‐time framework. The usefulness and information on variographic modeling based on scores are illustrated. A new test for randomness of a variogram is presented. Copyright © 2013 John Wiley & Sons, Ltd.