Process optimization : a statistical approach / Enrique del Castillo.

Includes bibliographical references (p. 445-454) and index.

1. An overview of empirical process optimization -- 2. Optimization of first order models -- 3. Experimental designs for first order models -- 4. Analysis and optimization of second order models -- 5. Experimental designs for second order models -- 6. Statistical inference in first order RSM optimization -- 7. Statistical inference in second order RSM optimization -- 8. Bias vs. variance -- 9. Robust parameter design -- 10. Robust optimization** -- 11. Introduction to Bayesian inference -- 12. Bayesian methods for process optimization -- 13. Simulation optimization -- 14. Kriging and computer experiments -- App. A. Basics of linear regression -- App. B. Analysis of variance -- App. C. Matrix algebra and optimization results -- App. D. Some probability results used in Bayesian inference.

"Process Optimization: A Statistical Approach is a textbook for a course in experimental optimization techniques for industrial production processes and other "noisy" systems where the main emphasis is process optimization. The book can also be used as a reference text by Industrial, Quality and Process Engineers and Applied Statisticians working in industry, in particular, in semiconductor/electronics manufacturing and in biotech manufacturing industries."--BOOK JACKET.