Sensitivity Analysis in Practice

Sensitivity analysis should be considered a pre-requisite for statistical model building in any scientific discipline where modelling takes place. For a non-expert, choosing the method of analysis for their model is complex, and depends on a number of factors. This  book guides the non-expert through their problem in order to enable them to choose and apply the most appropriate method. It offers a review of the state-of-the-art in sensitivity analysis, and is suitable for a wide range of practitioners. It is focussed on the use of SIMLAB – a widely distributed freely-available sensitivity analysis software package developed by the authors – for solving problems in sensitivity analysis of statistical models.

Other key features:

• Provides an accessible overview of the current most widely used methods for sensitivity analysis.
• Opens with a detailed worked example to explain the motivation behind the book.
• Includes a range of examples to help illustrate the concepts discussed.
• Focuses on implementation of the methods in the software SIMLAB - a freely-available sensitivity analysis software package developed by the authors.
• Contains a large number of references to sources for further reading.
• Authored by the leading authorities on sensitivity analysis.

“"...an interesting and informative book..." ( Technometrics , May 2005) "...provides an accessible overview of the most widely used sensitivity analysis methods." ( Zentralblatt Math , Vol.1049, 2004) "...well written..." (Statistical Methods in Medical Research, Vol 14 2005)”

"...an interesting and informative book..." (Technometrics, May 2005)

"...provides an accessible overview of the most widely used sensitivity analysis methods." (Zentralblatt Math, Vol.1049, 2004)

"...well written..." (Statistical Methods in Medical Research, Vol 14 2005)

Andrea Saltelli, Joint Research Centre of the European Commission, Ispra, Unit of Applied Statistics and Econometrics. Presently leading the Econometric and Applied Statistics Unit of the Joint Research Centre, lead author Professor Saltelli has published many articles in numerous journals over the last 30 years. He is also the main author and main editor of two previous books (both for Wiley).

Sensitivity analysis is the study of how variation in the output of a statistical model can be apportioned, qualitatively or quantitatively, to different sources of variation. It should be considered a pre-requisite for statistical model building in any scientific discipline where modelling takes place. Choosing the most appropriate method of sensitivity analysis for a particular model can be complex, and depends on a number of factors.

Sensitivity Analysis in Practice guides applied scientists through their modelling problem enabling them to choose and apply the most appropriate sensitivity analysis method. It offers a review of the state-of-the-art in sensitivity analysis, and is suitable for a wide range of practitioners. It includes discussion of implementation of the methods using SIMLAB – freely available sensitivity analysis software.

• Provides an accessible overview of the most widely used sensitivity analysis methods.

• Covers a range of methodologies, including variance-based methods, Bayesian uncertainty estimation, selected screening methods and Monte Carlo filtering.

• Opens with a detailed worked example to explain the motivation behind methods described.

• Includes a range of further worked examples to illustrate the practical applications.

• Discusses implementation of the methods using SIMLAB – freely available sensitivity analysis software.

• Contains a large number of references to sources for further reading.

• Supported by a Website featuring a SIMLAB download, data sets and additional material.

Sensitivity Analysis in Practice is primarily aimed at researchers and practitioners working in scientific modelling from statistics, mathematics, medicine, and environmental science. The book is suitable for applied scientists working with statistical models in virtually any discipline, including economics, biology, chemistry and engineering, as well as graduate students of statistical modelling.

Sensitivity analysis is the study of how variation in the output of a statistical model can be apportioned, qualitatively or quantitatively, to different sources of variation. It should be considered a pre-requisite for statistical model building in any scientific discipline where modelling takes place. Choosing the most appropriate method of sensitivity analysis for a particular model can be complex, and depends on a number of factors. Sensitivity Analysis in Practice guides applied scientists through their modelling problem enabling them to choose and apply the most appropriate sensitivity analysis method. It offers a review of the state-of-the-art in sensitivity analysis, and is suitable for a wide range of practitioners. It includes discussion of implementation of the methods using SIMLAB freely available sensitivity analysis software. Provides an accessible overview of the most widely used sensitivity analysis methods. Covers a range of methodologies, including variance-based methods, Bayesian uncertainty estimation, selected screening methods and Monte Carlo filtering. Opens with a detailed worked example to explain the motivation behind methods described. Includes a range of further worked examples to illustrate the practical applications. Discusses implementation of the methods using SIMLAB freely available sensitivity analysis software. Contains a large number of references to sources for further reading. Supported by a Website featuring a SIMLAB download, data sets and additional material. Sensitivity Analysis in Practice is primarily aimed at researchers and practitioners working in scientific modelling from statistics, mathematics, medicine, and environmental science. The book is suitable for applied scientists working with statistical models in virtually any discipline, including economics, biology, chemistry and engineering, as well as graduate students of statistical modelling.

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