PREDICTION IN REPEATED-MEASURES MODELS WITH ENGINEERING APPLICATIONS

This article focuses on the problem of predicting future measurements on a statistical unit given past measurements on the same and other si milar units. We introduce a conditional predictor that uses the inform ation contained in previous measurements. The prediction technique is based on the iterative EM algorithm, but a noniterative variant is als o provided. We use the sample-reuse methodology to select an appropria te predictor. The technique is illustrated in three engineering applic ations. The first considers prediction in the context of marketing for bucking in automatic forest harvesters. In fatigue-crack-growth data, the interest is in predicting the future crack-growth development of the test unit, and the third application concerns evaluation of pulp f rom the point of view of its papermaking potential.