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 Scheffé'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.