A new interpretation and validation of variance based importance measures for models with correlated inputs

Abstract In order to explore the contributions by correlated input variables to the variance of the output, a novel interpretation framework of importance measure indices is proposed for a model with correlated inputs, which includes the indices of the total correlated contribution and the total uncorrelated contribution. The proposed indices accurately describe the connotations of the contributions by the correlated input to the variance of output, and they can be viewed as the complement and correction of the interpretation about the contributions by the correlated inputs presented in “Estimation of global sensitivity indices for models with dependent variables, Computer Physics Communications, 183 (2012) 937–946”. Both of them contain the independent contribution by an individual input. Taking the general form of quadratic polynomial as an illustration, the total correlated contribution and the independent contribution by an individual input are derived analytically, from which the components and their origins of both contributions of correlated input can be clarified without any ambiguity. In the special case that no square term is included in the quadratic polynomial model, the total correlated contribution by the input can be further decomposed into the variance contribution related to the correlation of the input with other inputs and the independent contribution by the input itself, and the total uncorrelated contribution can be further decomposed into the independent part by interaction between the input and others and the independent part by the input itself. Numerical examples are employed and their results demonstrate that the derived analytical expressions of the variance-based importance measure are correct, and the clarification of the correlated input contribution to model output by the analytical derivation is very important for expanding the theory and solutions of uncorrelated input to those of the correlated one.

[1]  Herschel Rabitz,et al.  Sixth International Conference on Sensitivity Analysis of Model Output Global Sensitivity Analysis for Systems with Independent and / or Correlated Inputs , 2013 .

[2]  Bruno Sudret,et al.  Global sensitivity analysis using polynomial chaos expansions , 2008, Reliab. Eng. Syst. Saf..

[3]  Zhenzhou Lu,et al.  A new algorithm for variance based importance analysis of models with correlated inputs , 2013 .

[4]  Andrea Saltelli,et al.  Sensitivity Analysis for Importance Assessment , 2002, Risk analysis : an official publication of the Society for Risk Analysis.

[5]  Shoufan Fang,et al.  Estimation of sensitivity coefficients of nonlinear model input parameters which have a multinormal distribution , 2004 .

[6]  Zhenzhou Lu,et al.  Global Sensitivity Analysis Using Moving Least Squares for Models with Correlated Parameters , 2011 .

[7]  Enrique F. Castillo,et al.  Sensitivity analysis in optimization and reliability problems , 2008, Reliab. Eng. Syst. Saf..

[8]  Lu Zhenzhou,et al.  Investigation of the uncertainty of the in-plane mechanical properties of composite laminates , 2012 .

[9]  Thierry Alex Mara,et al.  Variance-based sensitivity indices for models with dependent inputs , 2012, Reliab. Eng. Syst. Saf..

[10]  George Z. Gertner,et al.  Uncertainty and sensitivity analysis for models with correlated parameters , 2008, Reliab. Eng. Syst. Saf..

[11]  A. Cohen An Introduction to Probability Theory and Mathematical Statistics , 1979 .

[12]  Vincenzo Ilario Carbone,et al.  An improvement of the response surface method , 2011 .

[13]  Saltelli Andrea,et al.  Global Sensitivity Analysis: The Primer , 2008 .

[14]  H. Gomes,et al.  COMPARISON OF RESPONSE SURFACE AND NEURAL NETWORK WITH OTHER METHODS FOR STRUCTURAL RELIABILITY ANALYSIS , 2004 .

[15]  Zhenzhou Lu,et al.  A new method on ANN for variance based importance measure analysis of correlated input variables , 2012 .

[16]  Ilya M. Sobol,et al.  Theorems and examples on high dimensional model representation , 2003, Reliab. Eng. Syst. Saf..

[17]  Emanuele Borgonovo,et al.  A new uncertainty importance measure , 2007, Reliab. Eng. Syst. Saf..

[18]  Ericka Stricklin-Parker,et al.  Ann , 2005 .

[19]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[20]  A. Saltelli,et al.  Importance measures in global sensitivity analysis of nonlinear models , 1996 .

[21]  Terje Aven,et al.  On the use of uncertainty importance measures in reliability and risk analysis , 2010, Reliab. Eng. Syst. Saf..

[22]  Xiaoping Du,et al.  Sensitivity Analysis with Mixture of Epistemic and Aleatory Uncertainties , 2007 .

[23]  Stefano Tarantola,et al.  Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models , 2004 .

[24]  Andrea Saltelli,et al.  Guest editorial: The role of sensitivity analysis in the corroboration of models and itslink to model structural and parametric uncertainty , 1997 .

[25]  Wenrui Hao,et al.  Importance measure of correlated normal variables and its sensitivity analysis , 2012, Reliab. Eng. Syst. Saf..

[26]  Zhenzhou Lu,et al.  Importance analysis for models with correlated input variables by the state dependent parameters method , 2011, Comput. Math. Appl..

[27]  Paola Annoni,et al.  Estimation of global sensitivity indices for models with dependent variables , 2012, Comput. Phys. Commun..

[28]  Kjell A. Doksum,et al.  Nonparametric Estimation of Global Functionals and a Measure of the Explanatory Power of Covariates in Regression , 1995 .

[29]  Zhen-zhou Lü,et al.  Moment-independent importance measure of basic random variable and its probability density evolution solution , 2010 .

[30]  Ronald L. Iman,et al.  Assessing hurricane effects. Part 2. Uncertainty analysis , 2002, Reliab. Eng. Syst. Saf..

[31]  B. M. Fulk MATH , 1992 .

[32]  S. Hora,et al.  A Robust Measure of Uncertainty Importance for Use in Fault Tree System Analysis , 1990 .

[33]  Ronald L. Iman,et al.  Assessing hurricane effects. Part 1. Sensitivity analysis , 2002, Reliab. Eng. Syst. Saf..