Saltelli, Campolongo, Ratto

Sensitivity Analysis in Practice

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 232
• Erscheinungstermin: 24. März 2004
• Englisch
• Abmessung: 235mm x 157mm x 18mm
• Gewicht: 535g
• ISBN-13: 9780470870938
• ISBN-10: 0470870931
• Artikelnr.: 21449335

Autorenporträt

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).

Inhaltsangabe

PREFACE. 1. A WORKED EXAMPLE. 1.1 A simple model. 1.2 Modulus version of
the simple model. 1.3 Six-factor version of the simple model. 1.4 The
simple model 'by groups'. 1.5 The (less) simple correlated-input model. 1.6
Conclusions. 2. GLOBAL SENSITIVITY ANALYSIS FOR IMPORTANCE ASSESSMENT. 2.1
Examples at a glance. 2.2 What is sensitivity analysis? 2.3 Properties of
an ideal sensitivity analysis method. 2.4 Defensible settings for
sensitivity analysis. 2.5 Caveats. 3. TEST CASES. 3.1 The jumping man.
Applying variance-based methods. 3.2 Handling the risk of a financial
portfolio: the problem of hedging. Applying Monte Carlo filtering and
variance-based methods. 3.3 A model of fish population dynamics. Applying
the method of Morris. 3.4 The Level E model. Radionuclide migration in the
geosphere. Applying variance-based methods and Monte Carlo filtering. 3.5
Two spheres. Applying variance based methods in estimation/calibration
problems. 3.6 A chemical experiment. Applying variance based methods in
estimation/calibration problems. 3.7 An analytical example. Applying the
method of Morris. 4. THE SCREENING EXERCISE. 4.1 Introduction. 4.2 The
method of Morris. 4.3 Implementing the method. 4.4 Putting the method to
work: an analytical example. 4.5 Putting the method to work: sensitivity
analysis of a fish population model. 4.6 Conclusions. 5. METHODS BASED ON
DECOMPOSING THE VARIANCE OF THE OUTPUT. 5.1 The settings. 5.2 Factors
Prioritisation Setting. 5.3 First-order effects and interactions. 5.4
Application of Si to Setting 'Factors Prioritisation'. 5.5 More on variance
decompositions. 5.6 Factors Fixing (FF) Setting. 5.7 Variance Cutting (VC)
Setting. 5.8 Properties of the variance based methods. 5.9 How to compute
the sensitivity indices: the case of orthogonal input. 5.9.1 A digression
on the Fourier Amplitude Sensitivity Test (FAST). 5.10 How to compute the
sensitivity indices: the case of non-orthogonal input. 5.11 Putting the
method to work: the Level E model. 5.11.1 Case of orthogonal input factors.
5.11.2 Case of correlated input factors. 5.12 Putting the method to work:
the bungee jumping model. 5.13 Caveats. 6. SENSITIVITY ANALYSIS IN
DIAGNOSTIC MODELLING: MONTE CARLO FILTERING AND REGIONALISED SENSITIVITY
ANALYSIS, BAYESIAN UNCERTAINTY ESTIMATION AND GLOBAL SENSITIVITY ANALYSIS.
6.1 Model calibration and Factors Mapping Setting. 6.2 Monte Carlo
filtering and regionalised sensitivity analysis. 6.2.1 Caveats. 6.3 Putting
MC filtering and RSA to work: the problem of hedging a financial portfolio.
6.4 Putting MC filtering and RSA to work: the Level E test case. 6.5
Bayesian uncertainty estimation and global sensitivity analysis. 6.5.1
Bayesian uncertainty estimation. 6.5.2 The GLUE case. 6.5.3 Using global
sensitivity analysis in the Bayesian uncertainty estimation. 6.5.4
Implementation of the method. 6.6 Putting Bayesian analysis and global SA
to work: two spheres. 6.7 Putting Bayesian analysis and global SA to work:
a chemical experiment. 6.7.1 Bayesian uncertainty analysis (GLUE case).
6.7.2 Global sensitivity analysis. 6.7.3 Correlation analysis. 6.7.4
Further analysis by varying temperature in the data set: fewer interactions
in the model. 6.8 Caveats. 7. HOW TO USE SIMLAB. 7.1 Introduction. 7.2 How
to obtain and install SIMLAB. 7.3 SIMLAB main panel. 7.4 Sample generation.
7.4.1 FAST. 7.4.2 Fixed sampling. 7.4.3 Latin hypercube sampling (LHS).
7.4.4 The method of Morris. 7.4.5 Quasi-Random LpTau. 7.4.6 Random. 7.4.7
Replicated Latin Hypercube (r-LHS). 7.4.8 The method of Sobol'. 7.4.9 How
to induce dependencies in the input factors. 7.5 How to execute models. 7.6
Sensitivity analysis. 8. FAMOUS QUOTES: SENSITIVITY ANALYSIS IN THE
SCIENTIFIC DISCOURSE. REFERENCES. INDEX.

Rezensionen

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

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

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