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Sensitivity Analysis in Practice

A Guide to Assessing Scientific Models

Andrea Saltelli, Francesca Campolongo, Marco Ratto, et al.

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John Wiley & Sons img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik

Beschreibung

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.

Rezensionen

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

Angew. Wahrscheinlichkeitsrechn. u. Statistik / Modelle, Engineering Statistics, Applied Probability & Statistics - Models, Statistics, Sensitivitätsanalyse, Statistik, Biometrie, Statistik in den Ingenieurwissenschaften, Biometrics