Principal component regression for data containing outliers and missing elements

A methodology is presented to construct an expectation robust algorithm for principal component regression. The presented method is the first multivariate regression method which can resist outliers and which can cope with missing elements in the data simultaneously. Simulations and an example illustrate the good statistical properties of the method.