A Modern Theory of Factorial Design (Springer Series in Statistics)

Factorial design plays a fundamental role in efficient and economic experimentation with multiple input variables and is extremely popular in various fields of application, including engineering, agriculture, medicine and life sciences. Factorial experiments are often used in case studies in quality management and Design for Six Sigma (DFSS). The last twenty years have witnessed a significant growth of interest in optimal factorial designs, under possible model uncertainty, via the minimum aberration and related criteria. The present book gives, for the first time in book form, a comprehensive and up-to-date account of this modern theory. Many major classes of designs are covered in the book. While maintaining a high level of mathematical rigor, it also provides extensive design tables for research and practical purposes. In order to equip the readers with the necessary background, some foundational concepts and results are developed in Chapter 2. Apart from being useful to researchers and practitioners, the book can form the core of a graduate level course in experimental design. It can also be used for courses in combinatorial designs or combinatorial mathematics.

Factorial design plays a fundamental role in efficient and economic experimentation with multiple input variables and is extremely popular in various fields of application, including engineering, agriculture, medicine and life sciences. Factorial experiments are often used in case studies in quality management and Design for Six Sigma (DFSS). The last twenty years have witnessed a significant growth of interest in optimal factorial designs, under possible model uncertainty, via the minimum aberration and related criteria. The present book gives, for the first time in book form, a comprehensive and up-to-date account of this modern theory. Many major classes of designs are covered in the book. While maintaining a high level of mathematical rigor, it also provides extensive design tables for research and practical purposes. In order to equip the readers with the necessary background, some foundational concepts and results are developed in Chapter 2. Apart from being useful to researchers and practitioners, the book can form the core of a graduate level course in experimental design. It can also be used for courses in combinatorial designs or combinatorial mathematics.

Rahul Mukerjee is a Professor of Statistics at the Indian Institute of Management Calcutta. Formerly, he was a Professor at the Indian Statistical Institute. He is a co-author of four other research monographs including two from Springer and one from Wiley. A Fellow of the Institute of Mathematical Statistics and the Indian National Science Academy, Professor Mukerjee has  served on the editorial boards of several international journals. He is a recipient of the S.S. Bhatnagar Award, the most well-known scientific honor from the Government of India.

C. F. Jeff Wu is Coca Cola Chair Professor in Engineering Statistics at Georgia Institute of Technology. Prior to 2003, he taught statistics at U. of Wisconsin, U. of Waterloo and U. of Michigan. He wrote with M. Hamada the applied design text Experiments: Planning, Analysis and Parameter Design Optimization by Wiley in 2000. He has served on various editorial boards. For his work in theory and methodology, including major work on design of experiments, he has won numerous awards and professional fellowships, including the COPSS Award and membership on the U.S. National Academy of Engineering.