Prediction in Repeated-Measures Models With Engineering Applications

Abstract

This article focuses on the problem of predicting future measurements on a statistical unit given past measurements on the same and other similar units. We introduce a conditional predictor that uses the information contained in previous measurements. The prediction technique is based on the iterative EM algorithm, but a noniterative variant is also provided. We use the sample-reuse methodology to select an appropriate predictor. The technique is illustrated in three engineering applications. 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 from the point of view of its papermaking potential.

KEY WORDS:

• Conditional prediction
• EM algorithm
• Mixed effects
• Parsimonious covariance structure