Growth Models for Repeated Measurement Mixture Experiments: Optimal Designs for Parameter Estimation and Growth Prediction
The present study focuses on the problems of parameter estimation and growth prediction in a quadratic growth model based on repeated measurements of growth, where the parameters in the model are assumed to be functions of ‘treatments’ which are treated as mixtures. The study concentrates not only on the optimality aspects of designs for most efficient estimation of the parameters but also on optimal prediction of growth at designated time points.
KeywordsD- and A-optimal designs Mixtures Parameter estimation Prediction Quadratic growth model Repeated measurements
The authors thank the anonymous referees for their fruitful comments. Much of the final shape of the manuscript rested on their insightful and helpful suggestions and recommendations towards better presentation of our ideas.
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