MIPLS2: Exploiting PLS2 to impute missing values in a two-block system with multiple response variables

MIPLS2 effectively addresses complex missing data scenarios. Regression results using PLS1 and PLS2 with imputed response variables demonstrate that MIPLS2 has a smaller positive impact on prediction outputs compared to competing imputation methods. This remains consistent for both random missing values (Case Yrand) and systematic missing values (Case Ysyst). These findings underscore MIPLS2 as a robust solution for imputing missing values in systems where the values of response variables are labile or systematically absent.