Optimal Design of Experiments

Devoted to a unified optimality theory, merging three otherwise distinct mathematical disciplines to embrace an astonishingly wide variety of design problems. Outlines typical settings, namely D-, A-, and E-optimal, polynominal regression designs, Bayesian designs, structures for model discrimination, balanced incomplete block arrangements or rotatable response surface designs. The design problems stem from statistics but are solved using special tools from linear algebra and convex analysis.

Optimal Design of Experiments offers a rare blend of linear algebra, convex analysis, and statistics. Since the book's initial publication in 1993, readers have used its methods to derive optimal designs on the circle, optimal mixture designs, and optimal designs in other statistical models.