Title: Bootstrap methods for sensitivity analysis of computer models (UQ/V&V Seminar) Speaker: Curtis Storlie, UNM Dept. of Mathematics and Statistics Date/Time: Thursday, March 6, 2:00-3:00 (NM), 1:00-2:00 (CA) Location: CSRI Building, Room 90 (Sandia NM), Building 915, Room 107 (CA) Brief Abstract: The understanding of many physical and engineering phenomena of interest involves running complex computational models (computer codes). With any problem of this type it is important to understand the relationships between the input variables (whose values are often imprecisely known) and the output. The goal of sensitivity analysis (SA) is to study this relationship and identify the most significant factors or variables affecting the results of the model. In this presentation we suggest an improvement on existing methods for SA of complex computer models when the model is too computationally expensive for a standard Monte-Carlo analysis. In these situations a meta-model or surrogate model can be used to estimate the necessary sensitivity index for each input. A sensitivity index is a measure of the variance in the response that is due to the input. The existing approaches to this problem either do not work well with a large number of input variables or do not satisfactorily deal with estimation error. Here we propose a new approach to variance index estimation which appears to incorporate satisfactory solutions to these drawbacks. The approach uses stepwise regression as well as boostrap methods to generate confidence intervals on the sensitivity indices. Several nonparametric regression procedures such as locally weighted polynomial regression (LOESS), additive models (GAM’s), projection pursuit, and recursive partitioning are considered as well as metamodels such as multivariate adaptive regression splines (MARS), random forests, and the gradient boosting method. An approach for calculating statistical properties of the bootstrap estimator will also be discussed. Several examples will illustrate the utility of this approach in practice.CSRI POC: Laura Painton Swiler, (505) 844-8093 |