A review of techniques for parameter sensitivity analysis of environmental models

Mathematical models are utilized to approximate various highly complex engineering, physical, environmental, social, and economic phenomena. Model parameters exerting the most influence on model results are identified through a ‘sensitivity analysis’. A comprehensive review is presented of more than a dozen sensitivity analysis methods. This review is intended for those not intimately familiar with statistics or the techniques utilized for sensitivity analysis of computer models. The most fundamental of sensitivity techniques utilizes partial differentiation whereas the simplest approach requires varying parameter values one-at-a-time. Correlation analysis is used to determine relationships between independent and dependent variables. Regression analysis provides the most comprehensive sensitivity measure and is commonly utilized to build response surfaces that approximate complex models.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42691/1/10661_2004_Article_BF00547132.pd

Rank correlation plots for use with correlated input variables in simulation studies

A method for inducing a desired rank correlation matrix on multivariate input vectors for simulation studies has recently been developed by Iman and Conover (SAND 80-0157). The primary intention of this procedure is to produce correlated input variables for use with computer models. Since this procedure is distribution free and allows the exact marginal distributions to remain intact, it can be used with any marginal distributions for which it is reasonable to think in terms of correlation. A series of rank correlation plots based on this procedure when the marginal distributions are normal, lognormal, uniform, and loguniform is presented. These plots provide a convenient tool for both aiding the modeler in determining the degree of dependence among variables (rather than guessing) and communicating with the modeler the effect of different correlation assumptions. 12 figures, 10 tables

An iterative algorithm to produce a positive definite correlation matrix from an approximate correlation matrix (with a program user's guide)

This report contains an explanation of an algorithm that, when executed, will operate on any symmetric approximate correlation matrix by iteratively adjusting the eigenvalues of this matrix. The objective of this algorithm is to produce a valid, positive definite, correlation matrix. Also a description of a program (called POSDEF) which implements the algorithm is given

Sensitivity-analysis techniques: self-teaching curriculum

This self teaching curriculum on sensitivity analysis techniques consists of three parts: (1) Use of the Latin Hypercube Sampling Program (Iman, Davenport and Ziegler, Latin Hypercube Sampling (Program User's Guide), SAND79-1473, January 1980); (2) Use of the Stepwise Regression Program (Iman, et al., Stepwise Regression with PRESS and Rank Regression (Program User's Guide) SAND79-1472, January 1980); and (3) Application of the procedures to sensitivity and uncertainty analyses of the groundwater transport model MWFT/DVM (Campbell, Iman and Reeves, Risk Methodology for Geologic Disposal of Radioactive Waste - Transport Model Sensitivity Analysis; SAND80-0644, NUREG/CR-1377, June 1980: Campbell, Longsine, and Reeves, The Distributed Velocity Method of Solving the Convective-Dispersion Equation, SAND80-0717, NUREG/CR-1376, July 1980). This curriculum is one in a series developed by Sandia National Laboratories for transfer of the capability to use the technology developed under the NRC funded High Level Waste Methodology Development Program