title: Multivariate linear regression with missing values.
abstract: This contribution presents and discusses an efficient algorithm for multivariate linear regression analysis of data sets with missing values. The algorithm is based on the insight that multivariate linear regression can be formulated as a set of individual univariate linear regressions. All available information is used and the calculations are explicit. The only restriction is that the independent variable matrix has to be non-singular. There is no need for imputation of interpolated or otherwise guessed values which require subsequent iterative refinement.
authors.affiliation: Department of Chemistry, University of Newcastle, Newcastle, NSW 2308, Australia.
authors.forename: Yaser
authors.initials: Y
authors.lastname: Beyad
authors.forename: Marcel
authors.initials: M
authors.lastname: Maeder
keywords: Linear regression; Missing values