Experiments: Planning, Analysis, and Optimization
Wu (statistics and industrial and operations engineering, Georgia Tech) and
Hamada (statistical science, Los Alamos National Laboratory) introduce some
recent discoveries in designing and analyzing experiments to improve industrial
products and processes, and their application to system optimization, robustness,
and treatment comparison in a number of fields. They describe robust parameter
design, improving reliability, analyzing non-normal data, experiments with
complex aliasing, multilevel designs, minimum aberrations designs, orthogonal
arrays, and other methods.
A Modern Theory of Factorial Design
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. Mukerjee (operations management, Indian Institute of Management) and Wu (statistics and industrial and operations engineering, Georgia Tech) give, 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.