Name
Abstract | ||
|---|---|---|
In many situations, simulation of complex phenomena requires a large number of inputs and is computationally expensive. Identifying the inputs that most impact the system so that these factors can be further investigated can be a critical step in the scientific endeavor. In computer experiments, it is common to use a Gaussian spatial process to model the output of the simulator. In this article we introduce a new, simple method for identifying active factors in computer screening experiments. The approach is Bayesian and only requires the generation of a new inert variable in the analysis; however, in the spirit of frequentist hypothesis testing, the posterior distribution of the inert factor is used as a reference distribution against which the importance of the experimental factors can be assessed. The methodology is demonstrated on an application in material science, a computer experiment from the literature, and simulated examples. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1198/004017006000000228 | TECHNOMETRICS |
Keywords | Field | DocType |
computer simulation,Latin hypercube,random field,screening,spatial process | Econometrics,Computer experiment,Frequentist inference,Feature selection,Posterior probability,Gaussian,Gaussian process,Statistics,Statistical hypothesis testing,Latin hypercube sampling,Mathematics | Journal |
Volume | Issue | ISSN |
48 | 4 | 0040-1706 |
Citations | PageRank | References |
23 | 2.85 | 2 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Crystal Linkletter | 1 | 30 | 3.82 |
| Derek Bingham | 2 | 126 | 27.32 |
| Nicholas Hengartner | 3 | 23 | 2.85 |
| David Higdon | 4 | 61 | 14.71 |
| Kenny Q. Ye | 5 | 35 | 5.21 |