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Growth Models for Repeated Measurement Mixture Experiments: Optimal Designs for Parameter Estimation and Growth Prediction

In: Advances in Growth Curve and Structural Equation Modeling

Author

Listed:
  • Manisha Pal

    (Calcutta University)

  • Nripes K. Mandal

    (Calcutta University)

  • Bikas K. Sinha

    (Indian Statistical Institute)

Abstract

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.

Suggested Citation

  • Manisha Pal & Nripes K. Mandal & Bikas K. Sinha, 2018. "Growth Models for Repeated Measurement Mixture Experiments: Optimal Designs for Parameter Estimation and Growth Prediction," Springer Books, in: Ratan Dasgupta (ed.), Advances in Growth Curve and Structural Equation Modeling, pages 81-94, Springer.
    • Handle: RePEc:spr:sprchp:978-981-13-0980-9_6
    DOI: 10.1007/978-981-13-0980-9_6
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