Global Sensitivity Analysis: The Primer | 
| Authors: Andrea Saltelli, Marco Ratto, Terry Andres, Francesca Campolongo, Jessica Cariboni, Debora Gatelli, Michaela Saisana, Stefano Tarantola
Publisher: Wiley-Interscience
Category: Book
List Price: $110.00
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Avg. Customer Rating:  1 reviews
Sales Rank: 562396
Media: Hardcover
Number Of Items: 1
Pages: 304
Shipping Weight (lbs): 1.1
Dimensions (in): 9.2 x 6.2 x 0.9
ISBN: 0470059974
Dewey Decimal Number: 003
EAN: 9780470059975
ASIN: 0470059974
Publication Date: March 7, 2008
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Product Description
Complex mathematical and computational models are used in all areas of society and technology and yet model based science is increasingly contested or refuted, especially when models are applied to controversial themes in domains such as health, the environment or the economy. More stringent standards of proofs are demanded from model-based numbers, especially when these numbers represent potential financial losses, threats to human health or the state of the environment. Quantitative sensitivity analysis is generally agreed to be one such standard. Mathematical models are good at mapping assumptions into inferences. A modeller makes assumptions about laws pertaining to the system, about its status and a plethora of other, often arcane, system variables and internal model settings. To what extent can we rely on the model-based inference when most of these assumptions are fraught with uncertainties? Global Sensitivity Analysis offers an accessible treatment of such problems via quantitative sensitivity analysis, beginning with the first principles and guiding the reader through the full range of recommended practices with a rich set of solved exercises. The text explains the motivation for sensitivity analysis, reviews the required statistical concepts, and provides a guide to potential applications. The book: - Provides a self-contained treatment of the subject, allowing readers to learn and practice global sensitivity analysis without further materials.
- Presents ways to frame the analysis, interpret its results, and avoid potential pitfalls.
- Features numerous exercises and solved problems to help illustrate the applications.
- Is authored by leading sensitivity analysis practitioners, combining a range of disciplinary backgrounds.
Postgraduate students and practitioners in a wide range of subjects, including statistics, mathematics, engineering, physics, chemistry, environmental sciences, biology, toxicology, actuarial sciences, and econometrics will find much of use here. This book will prove equally valuable to engineers working on risk analysis and to financial analysts concerned with pricing and hedging.
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| Customer Reviews:
interesting treatment of model validation April 6, 2008
7 out of 8 found this review helpful
When I was working at Oak Ridge National Lab in the late 1970s, I worked with other scientists and statisticians on data and model validation. Toby Mitchell a specialist in experimental design was developing sampling techniques to use in model validation. He has since passed on. One of the techniques he used was Latin hypercube sampling. The authors of this text are from Italy and Canada. They have computer science and mathematical backgrounds but are not statisticians. Yet once you start reading book you will see that they appreciate both the deterministic and the stochastic aspects of modeling.
What they do, they call Global Sensitivity Analysis. They are very bright and are lucid in their explanations and description of philosophical issues. This is not something that those of us who do statistical modeling are very familiar with but it is important to know. It is especially gratifying to see that these authors are always wary of modeling assumptions and look for novel ways to test them. They point out that validating models is complicated. Many models that we construct are complex and even when we are aware of this and test aspects of the model. we often take some things for granted and accept aspects of the model as a given. I really enjoyed reading the afterword where these issues are well brought out.
Specific methods include th elementary effects method discussed in Chapter 3 that is based on the work of Max Morris in a 1991 paper in Technometrics. In Chapter 4, they cover variance-based methods which relies on the work of Sobol and others. They illustrate the applications of these methods with an infection dynamics model.
The main idea is to determine which factors affect the output variables as well as which interaction effects play a role. In the last chapter the authors make recommendations as to when to apply each technique.
At the beginning of the sections they raise questions that they answer in the section. This approach and the problem sets followed by complete and clear solutions makes the text readable and enjoyable even for novices.
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