Optimal Mixture Experiments [electronic resource] / by B.K. Sinha, N.K. Mandal, Manisha Pal, P. Das.

Chapter 1. Mixture Models and Mixture Designs: Scope of the Monograph -- Chapter 2. Optimal Regression Designs -- Chapter 3. Parameter Estimation in Linear and Quadratic Mixture Models -- Chapter 4. Optimal Mixture Designs for Estimation of Natural Parameters in Scheffé’s Model -- Chapter 5. Optimal Mixture Designs for Estimation of Natural Parameters in Scheffé’s Model under Constrained Factor Space -- Chapter 6. Optimal Mixture Designs for Estimation of Natural Parameters in Other Mixture Models -- Chapter 7. Optimal Designs for Estimation of Optimum Mixture in Scheffé’s Quadratic Model -- Chapter 8. More on Estimation of Optimum Mixture in Scheffé’s Quadratic Model -- Chapter 9. Optimal Designs for Estimation of Optimum Mixture in Scheffé’s Quadratic Model under Constrained Factor Space -- Chapter 10. Optimal Designs for Estimation of Optimum Mixture under Darroch-Waller and Log-Contrast Models -- Chapter 11. Applications of Mixture Experiments -- Chapter 12. Miscellaneous Topics: Robust mixtures, random regression coefficients, multiresponse experiments, mixture-amount models, blocking in mixture designs.

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.