Model Robust Optimal Designs for Kronecker Model for Mixture Experiments

Authors

  • Mahesh Kumar Panda Department of Statistics, Ravenshaw University, Cuttack, 753003, India.

DOI:

https://doi.org/10.3329/ijss.v24i1.72016

Keywords:

Canonical polynomial model, Kronecker model, Mixture experiment, Ill-conditioning, D-optimality, A-optimality.

Abstract

In comparison to Scheffè’s canonical polynomial models (S-models), the Kronecker models (K-models) for mixture experiments are symmetric, compact in notation, and based on the Kronecker algebra of vectors and matrices. Further, there is a corresponding transition from S-models to K-models in the form of model re-parameterization. In the literature, it has been recommended to use second-degree K-models in practice compared to the widely used second-degree S-models especially when the moment matrix is of an ill-conditioning type. The motivation of the present article is to discriminate between K-models and S-models in terms of the model-robust D- and A-optimality criteria. These optimality criteria are discussed when there is uncertainty in selecting an appropriate model out of two rival models for a mixture experiment.

International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 31-48

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Published

2024-03-28

How to Cite

Panda, M. K. . (2024). Model Robust Optimal Designs for Kronecker Model for Mixture Experiments. International Journal of Statistical Sciences, 24(1), 31–48. https://doi.org/10.3329/ijss.v24i1.72016

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Section

Original Articles