Shapley effects for global sensitivity analysis: Theory and computation

SIAM-ASA Journal on Uncertainty Quantification

Volumn 4, Issue 1, 2016, Pages 1060-1083

  Song, Eunhye ^a   Nelson, Barry L ^a   Staum, Jeremy ^a  

^a NORTHWESTERN UNIVERSITY   (United States)

Author keywords

Computer experiments; Global sensitivity; Shapley value

Indexed keywords

APPROXIMATION ALGORITHMS; BUDGET CONTROL; COMPUTATION THEORY; COMPUTATIONAL EFFICIENCY; GAME THEORY; UNCERTAINTY ANALYSIS;

COMPUTER EXPERIMENT; GLOBAL SENSITIVITY; GLOBAL SENSITIVITY ANALYSIS; MONTE CARLO ALGORITHMS; PROBABILISTIC DEPENDENCE; SHAPLEY VALUE; STRUCTURAL INTERACTIONS; VARIANCE-BASED GLOBAL SENSITIVITY ANALYSIS;

SENSITIVITY ANALYSIS;

EID: 85033580123     PISSN: None     EISSN: 21662525     Source Type: Journal    
DOI: 10.1137/15M1048070     Document Type: Article

References (30)

1. F. A. Albini, Estimating Wildfire Behavior and Eects, General technical report INT, Intermountain Forest and Range Experiment Station, Department of Agriculture, Forest Service, 1976
2. S. Asmussen and P. Glynn, Stochastic Simulation: Algorithms and Analysis: Algorithms and Analysis, Stoch. Model. Appl. Probab. 57, Springer, Berlin, 2007
3. L. Breiman, Random forests, Machine Learning, 45 (2001), pp. 5-32
4. M. C. Cario and B. L. Nelson, Autoregressive to anything: Time-series input processes for simulation, Oper. Res. Lett., 19 (1996), pp. 51-58
5. M. C. Cario and B. L. Nelson, Modeling and Generating Random Vectors with Arbitrary Marginal Distributions and Correlation Matrix, Tech. report, Department of Industrial Engineering and Man-agement Sciences, Northwestern University, Evanston, IL, 1997
6. F. Carreras and J. M. Gimenez, Power and potential maps induced by any semivalue: Some al-gebraic properties and computation by multilinear extensions, European J. Oper. Res., 211 (2011), pp. 148-159
7. J. Castro, D. Gomez, and J. A. T. Cazorla, Polynomial calculation of the Shapley value based on sampling, Comput. Oper. Res., 36 (2009), pp. 1726-1730
8. E. Catchpole and W. Catchpole, Modelling moisture damping for fire spread in a mixture of live and dead fuels, Internat. J. Wildland Fire, 1 (1991), pp. 101-106
9. G. Chastaing, F. Gamboa, and C. Prieur, Generalized Hoefiding-Sobol decomposition for depen-dent variables: Application to sensitivity analysis, Electron. J. Stat., 6 (2012), pp. 2420-2448, doi:10.1214/12-EJS749
10. R. E. Clark, A. S. Hope, S. Tarantola, D. Gatelli, P. E. Dennison, and M. A. Moritz, Sensitivity analysis of a fire spread model in a chaparral landscape, Fire Ecology, 4 (2008), pp. 1-13
11. P. Dubey, A. Neyman, and R. J. Weber, Value theory without efficiency, Math. Oper. Res., 6 (1981), pp. 122-128, doi:10.1287/moor.6.1.122
12. T. Homma and A. Saltelli, Importance measures in global sensitivity analysis of nonlinear models, Reliability Engineering System Safety, 52 (1996), pp. 1-17
13. G. Hooker, Generalized functional ANOVA diagnostics for high-dimensional functions of dependent variables, J. Comput. and Graph. Statist., 16 (2007), pp. 709-732
14. J. R. Jackson, Jobshop-like queueing systems, Management Sci., 50 (2004), pp. 1796-1802
15. J. P. C. Kleijnen, Factor screening in simulation experiments: Review of sequential bifurcation, in Advancing the Frontiers of Simulation, C. Alexopoulos, D. Goldsman, and J. R. Wilson, eds., Internat. Ser. Oper. Res. Management Sci. 133, Springer, New York, 2009, pp. 153-167
16. R. Nelsen, An Introduction to Copulas, Lecture Notes in Statist., Springer, New York, 2013
17. J. E. Oakley and A. O'Hagan, Probabilistic sensitivity analysis of complex models: A Bayesian ap-proach, J. Roy. Statist. Soc., Ser. B, 66 (2004), pp. 751-769
18. A. B. Owen, Sobol' indices and Shapley value, SIAM/ASA J. Uncertain. Quantif., 2 (2014), pp. 245-251, doi:10.1137/130936233
19. R. C. Rothermel, A Mathematical Model for Predicting Fire Spread in Wildland Fuels, USDA Forest Service Research Paper INT-155, Intermountain Forest and Range Experiment Station, Department of Agriculture, 1972
20. A. Saltelli, Making best use of model evaluations to compute sensitivity indices, Comput. Phys. Comm., 145 (2002), pp. 280-297
21. A. Saltelli, K. Chan, and E. Scott, Sensitivity Analysis: Gauging the Worth of Scientific Models, Wiley Ser. Probab. Stat., Wiley, New York, 2000
22. A. Saltelli, M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli, M. Saisana, and S. Tarantola, Global Sensitivity Analysis: The Primer, Wiley, New York, 2008
23. A. Saltelli and S. Tarantola, On the relative importance of input factors in mathematical models: Safety assessment for nuclear waste disposal, J. Amer. Statist. Assoc., 97 (2002), pp. pp. 702-709, http://www.jstor.org/stable/3085706
24. A. Saltelli, S. Tarantola, F. Campolongo, and M. Ratto, Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models, Wiley, New York, 2004
25. R. Salvador, J. Piol, S. Tarantola, and E. Pla, Global sensitivity analysis and scale effects of a fire propagation model used over Mediterranean shrublands, Ecological Model., 136 (2001), pp. 175-189
26. L. Shapley, A value for N-person games., Defense Technical Information Center, 1952
27. I. M. Sobol', Sensitivity estimates for nonlinear mathematical models, Math. Model. Comput. Experi-ment, 1 (1993), pp. 407-414
28. E. Song and B. L. Nelson, Quickly assessing contributions to input uncertainty, IIE Trans., 47 (2015), pp. 893-909, doi:10.1080/0740817X.2014.980869
29. Y. Sun, D. W. Apley, and J. Staum, Efficient nested simulation for estimating the variance of a conditional expectation., Oper. Res., 59 (2011), pp. 998-1007
30. C. E. Van Wagner, Metric Units and Conversion Factors for Forest Fire Quantities, Tech. report, Petawawa Forest Experiment Station, 1978