To tackle these challenges, we propose a novel thresholded multivariate principal component analysis (PCA) method for multichannel profile monitoring.

Statistics are often viewed as confusing and complicated, but multivariate data analysis (MVA) methods can be used to amass knowledge simply.

Abstract Multivariate analyses such as principal component analysis were among the first statistical methods employed to extract information from genetic markers.

We first show that PCA can be formulated as a regression-type optimization problem; sparse loadings are then obtained by imposing the lasso (elastic net) constraint on the regression coefficients.

The Q3 update also expands existing PCA and PLS multivariate models to extend the benefits of advanced analytics efforts beyond the data experts and across the organization.