Please use this identifier to cite or link to this item:
|Title:||Transformations and Invariance in the Sensitivity Analysis of Computer Experiments|
|Authors:||BORGONOVO Emanuele; TARANTOLA Stefano; PLISCHKE Elmar; MORRIS M.d.|
|Citation:||JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY vol. 76 no. 5 p. 925-947|
|Type:||Articles in periodicals and books|
|Abstract:||Monotonic transformations are widely employed in statistics and data analysis. In computer experiments they are often used to gain accuracy in the estimation of global sensitivity statistics. However, one faces the question of interpreting results obtained on the transformed data back on the original data. The situation is even more complex in computer experiments, because transformations alter the model input-output mapping and distort the estimators. This work demonstrates that the problem can be solved by utilizing statistics which are monotonic transformation invariant. To do so, we offer an investigation into the families of metrics either based on densities or on cumulative distribution functions that are monotonic transformation invariant and introduce a new generalized family of metrics. Numerical experiments show that transformations allow numerical convergence in the estimates of global sensitivity statistics, both invariant and not, in cases in which it would otherwise be impossible to obtain convergence. However, one fully exploits the increased numerical accuracy if the global sensitivity statistic is monotonic transformation invariant. Conversely, estimators of measures that do not have this invariance property might lead to misleading deductions.|
|JRC Directorate:||Joint Research Centre Corporate Activities|
Files in This Item:
There are no files associated with this item.
Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated.