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Seminario Emanuele Borgonovo 23/3/06 (IMQ Bocconi)
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AVVISO DI SEMINARIO
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Giovedi' 23 marzo 2006 alle ore 16:30 nella stanza 137
dell'Istituto di Metodi Quantitativi dell'Universita' Bocconi
(Viale Isonzo 25, Milano),
EMANUELE BORGONOVO, PH.D.
IMQ, Università Bocconi
terra' il seminario:
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PROBABILISTIC SENSITIVITY ANALYSIS
Abstract
According to the literature, sensitivity analysis techniques can be divided in
the categories of local methods [Borgonovo et al (2003), screening methods
[Kleijnen and Van Groenendall (2002)], non-parametric methods [Saltelli and
Marivoet (1990)], variance based methods [Saltelli et al (2000), Sobol’
(2001)] and moment independent approaches [Chun et al (2000), Borgonovo (2005)
and (2006).] Besides local methods, that aim at quantifying input influence at
a fixed input space point, all other methods aim at identifying the
contribution of uncertain inputs to output uncertainty. Their application
ranges from food-safety [Frey and Patil (2002)] to hurricane losses [Iman et
al (2005)]. Results and indications an analyst derives, however, depend on the
method selected for the study. After investigating the assumptions at the
basis of various indicator families we discuss the information they convey to
the analyst/decision maker. We start with nonparametric techniques, and then
present variance-based methods. By means of an example we show that
contradictory results may be obtained if one lets uncertainty coincide with
variance. We then examine moment-independent approaches to global sensitivity
analysis, i.e. techniques that look at the entire output distribution without
a particular reference to one of its moments. We detail the definition and
property of a new indicator [Borgonovo (2005)], and then compare it to results
that are obtained with the Chun-Ahn-Tuck indicator and the Kullback-Leibler
entropy measure [Chun et al (2000)]. In particular, we show that the Kullback-
Leibler information can fail in identifying parameter importance if the
support of the conditional induced distribution and the unconditional induced
distributions differ. We then present numerical results. They demonstrate that
both moment-independent and variance based indicators agree in identifying non-
influential parameters. However, differences in the ranking of the most
relevant factors evidence that inputs that influence variance the most are not
necessarily the ones that influence the output uncertainty distribution the
most. Perspectives of future research will be discussed.
REFERENCES
BORGONOVO E., APOSTOLAKIS G.E., TARANTOLA S. AND SALTELLI A.,
2003: "Comparison of Local and Global Sensitivity Analysis Techniques in
Probabilistic Safety Assessment," Reliability Engineering and System Safety,
79, pp. 175-185.
BORGONOVO E., 2005: "A New Uncertainty Importance Measure," Reliability
Engineering and System Safety, submitted.
BORGONOVO E., 2006: "Measuring Uncertainty Importance: Investigation and
Comparison of Alternative Approaches," Risk Analysis, submitted.
CHUN M-H., HAN S-J. AND TAK N-IL., 2000: "An uncertainty importance measure
using a distance metric for the change in a cumulative distribution function,"
Reliability Engineering and System Safety, 70, pp. 313-321.
FREY C. H. AND PATIL, 2002: "Identification and Review of Sensitivity Analysis
Methods," Risk Analysis, 22 (3), pp. 553-571.
KLEIJNEN J. P.C. AND VAN GROENENDAAL W.J.H., 2002: "Deterministic versus
stochastic sensitivity analysis in investment problems: An environmental case
study," European Journal of Operational Research, 141, pp. 8-20.
IMAN R.L., JOHNSON M. E. AND WATSON C.C. JR., 2005: "Sensitivity Analysis for
Computer Model Projections of Hurricane Losses," Risk Analysis, 25 (5),
pp.1277-1297.
SALTELLI A. AND MARIVOET J., 1990: "Non-parametric Statistics in Sensitivity
Analysis for Model Output: a Comparison of Selected Techniques," Reliability
Engineering and System Safety, 28, 229-253.
SALTELLI A., TARANTOLA S. AND CAMPOLONGO F., 2000: "Sensitivity Analysis as an
Ingredient of Modelling", Statistical Science, 19 (4), pp. 377-395.
SOBOL I.M., 2001: "Global sensitivity indices for nonlinear mathematical
models and their Monte Carlo estimates," Mathematics and Computers in
Simulation, 55(1), pp. 271-280.
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Tutti gli interessati sono invitati a partecipare.
Cordiali saluti,
Marco Bonetti
Marco Bonetti
Istituto di Metodi Quantitativi
Università Bocconi
Viale Isonzo 25
20135 Milano, Italy
(02) 58365670
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