Note publique d'information : The process of discovery in science and technology may require investigation of a
large number of features, such as factors, genes or molecules. In Screening, statistically
designed experiments and analyses of the resulting data sets are used to identify
efficiently the few features that determine key properties of the system under study.
This book brings together accounts by leading international experts that are essential
reading for those working in fields such as industrial quality improvement, engineering
research and development, genetic and medical screening, drug discovery, and computer
simulation of manufacturing systems or economic models. Our aim is to promote cross-fertilization
of ideas and methods through detailed explanations, a variety of examples and extensive
references. Topics cover both physical and computer simulated experiments. They include
screening methods for detecting factors that affect the value of a response or its
variability, and for choosing between various different response models. Screening
for disease in blood samples, for genes linked to a disease and for new compounds
in the search for effective drugs are also described. Statistical techniques include
Bayesian and frequentist methods of data analysis, algorithmic methods for both the
design and analysis of experiments, and the construction of fractional factorial designs
and orthogonal arrays. The material is accessible to graduate and research statisticians,
and to engineers and chemists with a working knowledge of statistical ideas and techniques.
It will be of interest to practitioners and researchers who wish to learn about useful
methodologies from within their own area as well as methodologies that can be translated
from one area to another. Angela Dean is Professor of Statistics at The Ohio State
University, USA. She is a Fellow of the American Statistical Association, the Institute
of Mathematical Statistics, and an elected member of the International Statistical
Institute. Her research focuses on the construction of efficient designs for factorial
experiments in industry and marketing. She is co-author of the textbook Design and
Analysis of Experiments and has served on the editorial boards of the Journal of the
Royal Statistical Society and Technometrics. Susan Lewis is a Professor of Statistics
at the University of Southampton, UK, and Deputy Director of the Southampton Statistical
Sciences Research Institute. She has research interests in screening, design algorithms
and the design and analysis of experiments in industry. She was awarded the Greenfield
Industrial Medal by the Royal Statistical Society in 2005. She has served the Society
as a Vice-President and a Member of Council, as well as a former Editor of the Journal
of the Royal Statistical Society, Series C (Applied Statistics)
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