Integration and exploration of High Dimensional data

Abstract

In modern Life Sciences, high dimensional correlated data matrices are often dealt with. Ordinary linear regression fail and PCA does not take into account the outcome variable(s). Also there might be large amount of orthogonal variation present. To deal with this, the O2PLS model (which is based on the PLS model) is derived. O2PLS is symmetric and predictive, with the covariance matrix playing an important role. The algorithm is given, and important remarks are made. A simulation study and real data analysis is conducted. A derivation for Probabilistic O2PLS is made, the estimation method being maximum likelihood. All calculations were done in R, code is available on request.Applied MathematicsStatisticsElectrical Engineering, Mathematics and Computer Scienc