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E-raamat: Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models

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(Joint Research Centre of the European Commission, Ispra, Italy), (Joint Research Centre of the European Commission, Ispra, Italy), (Joint Research Centre of the European), (Joint Research Centre of the European Commission, Ispra, Italy)
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  • Ilmumisaeg: 16-Jul-2004
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9780470870945
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Sensitivity Analysis in Practice: A Guide to Assessing Scientific ModelsSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 16-Jul-2004
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9780470870945
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Sensitivity analysis is the study of how variation in the output of a statistical model can be apportioned to different sources of variation. Saltelli (Joint Research Center of the European Commission, Italy) guides applied scientists through the process of choosing and applying the most appropriate sensitivity analysis method. He overviews the most widely used methods, such as Bayesian uncertainty estimation and Monte Carlo filtering, and discusses implementation of the methods using the sensitivity analysis software SIMLAB. A companion Web site offers a SIMLAB download, data sets, and additional material. The book is aimed at those working in scientific modeling. Annotation ©2004 Book News, Inc., Portland, OR (booknews.com)

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.

Arvustused

"...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)

PREFACE ix
1 A WORKED EXAMPLE 1(30)
1.1 A simple model
1(9)
1.2 Modulus version of the simple model
10(5)
1.3 Six-factor version of the simple model
15(7)
1.4 The simple model 'by groups'
22(3)
1.5 The (less) simple correlated-input model
25(3)
1.6 Conclusions
28(3)
2 GLOBAL SENSITWITY ANALYSIS FOR IMPORTANCE ASSESSMENT 31(32)
2.1 Examples at a glance
31(11)
2.2 What is sensitivity analysis?
42(5)
2.3 Properties of an ideal sensitivity analysis method
47(2)
2.4 Defensible settings for sensitivity analysis
49(7)
2.5 Caveats
56(7)
3 TEST CASES 63(28)
3.1 The jumping man. Applying variance-based methods
63(3)
3.2 Handling the risk of a financial portfolio: the problem of hedging. Applying Monte Carlo filtering and variance-based methods
66(5)
3.3 A model of fish population dynamics. Applying the method of Morris
71(6)
3.4 The Level E model. Radionuclide migration in the geosphere. Applying variance-based methods and Monte Carlo filtering
77(6)
3.5 Two spheres. Applying variance based methods in estimation/calibration problems
83(2)
3.6 A chemical experiment. Applying variance based methods in estimation/calibration problems
85(3)
3.7 An analytical example. Applying the method of Morris
88(3)
4 THE SCREENING EXERCISE 91(18)
4.1 Introduction
91(3)
4.2 The method of Morris
94(6)
4.3 Implementing the method
100(3)
4.4 Putting the method to work: an analytical example
103(1)
4.5 Putting the method to work: sensitivity analysis of a fish population model
104(3)
4.6 Conclusions
107(2)
5 METHODS BASED ON DECOMPOSING THE VARIANCE OF THE OUTPUT 109(42)
5.1 The settings
109(1)
5.2 Factors Prioritisation Setting
110(1)
5.3 First-order effects and interactions
111(1)
5.4 Application of Si to Setting 'Factors Prioritisation'
112(6)
5.5 More on variance decompositions
118(2)
5.6 Factors Fixing (FF) Setting
120(1)
5.7 Variance Cutting (VC) Setting
121(2)
5.8 Properties of the variance based methods
123(1)
5.9 How to compute the sensitivity indices: the case of orthogonal input
124(24)
5.9.1 A digression on the Fourier Amplitude Sensitivity Test (FAST)
132(1)
5.10 How to compute the sensitivity indices: the case of non-orthogonal input
132(4)
5.11 Putting the method to work: the Level E model
136(9)
5.11.1 Case of orthogonal input factors
137(7)
5.11.2 Case of correlated input factors
144(1)
5.12 Putting the method to work: the bungee jumping model
145(3)
5.13 Caveats
148(3)
6 SENSITIVITY ANALYSIS IN DIAGNOSTIC MODELLING: MONTE CARLO FILTERING AND REGIONALISED SENSITIVITY ANALYSIS, BAYESIAN UNCERTAINTY ESTIMATION AND GLOBAL SENSITIVITY ANALYSIS 151(42)
6.1 Model calibration and Factors Mapping Setting
151(2)
6.2 Monte Carlo filtering and regionalised sensitivity analysis
153(8)
6.2.1 Caveats
155(6)
6.3 Putting MC filtering and RSA to work: the problem of hedging a financial portfolio
161(6)
6.4 Putting MC filtering and RSA to work: the Level E test case
167(3)
6.5 Bayesian uncertainty estimation and global sensitivity analysis
170(8)
6.5.1 Bayesian uncertainty estimation
170(3)
6.5.2 The GLUE case
173(2)
6.5.3 Using global sensitivity analysis in the Bayesian uncertainty estimation
175(3)
6.5.4 Implementation of the method
178(1)
6.6 Putting Bayesian analysis and global SA to work: two spheres
178(6)
6.7 Putting Bayesian analysis and global SA to work: a chemical experiment
184(7)
6.7.1 Bayesian uncertainty analysis (GLUE case)
185(1)
6.7.2 Global sensitivity analysis
185(3)
6.7.3 Correlation analysis
188(1)
6.7.4 Further analysis by varying temperature in the data set: fewer interactions in the model
189(2)
6.8 Caveats
191(2)
7 HOW TO USE SIMLAG 193(12)
7.1 Introduction
193(1)
7.2 How to obtain and install SIMLAG
194(1)
7.3 SIMLAG main panel
194(3)
7.4 Sample generation
197(4)
7.4.1 FAST
198(1)
7.4.2 Fixed sampling
198(1)
7.4.3 Latin hypercube sampling (LHS)
198(1)
7.4.4 The method of Morris
199(1)
7.4.5 Quasi-Random LpTau
199(1)
7.4.6 Random
200(1)
7.4.7 Replicated Latin Hypercube (r-LHS)
200(1)
7.4.8 The method of Sobol'
200(1)
7.4.9 How to induce dependencies in the input factors
200(1)
7.5 How to execute models
201(1)
7.6 Sensitivity analysis
202(3)
8 FAMOUS QUOTES: SENSITIVITY ANALYSIS IN THE SCIENTIFIC DISCOURSE 205(6)
REFERENCES 211(6)
INDEX 217


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).
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