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The book dwells mainly on the optimality aspects of mixture
designs. As mixture models are a special case of regression models,
a general discussion on regression designs has been presented,
which includes topics like continuous designs, de la Garza
phenomenon, Loewner order domination, Equivalence theorems for
different optimality criteria and standard optimality results for
single variable polynomial regression and multivariate linear and
quadratic regression models. This is followed by a review of the
available literature on estimation of parameters in mixture models.
Based on recent research findings, the volume also introduces
optimal mixture designs for estimation of optimum mixing
proportions in different mixture models, which include Scheffe s
quadratic model, Darroch-Waller model, log- contrast model,
mixture-amount models, random coefficient models and multi-response
model. Robust mixture designs and mixture designs in blocks have
been also reviewed. Moreover, some applications of mixture designs
in areas like agriculture, pharmaceutics and food and beverages
have been presented. Familiarity with the basic concepts of design
and analysis of experiments, along with the concept of optimality
criteria are desirable prerequisites for a clear understanding of
the book. It is likely to be helpful to both theoreticians and
practitioners working in the area of mixture experiments."
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