A multivariate central limit theorem for randomized orthogonal array sampling designs in computer experiments
| dc.creator | Loh, Wei-Liem | |
| dc.date | 2007-08-05 | |
| dc.date.accessioned | 2026-07-07T08:22:15Z | |
| dc.date.available | 2026-07-07T08:22:15Z | |
| dc.description | Let $f:[0,1)^d \to {\mathbb R}$ be an integrable function. An objective of many computer experiments is to estimate $\int_{[0,1)^d} f(x) dx$ by evaluating f at a finite number of points in [0,1)^d. There is a design issue in the choice of these points and a popular choice is via the use of randomized orthogonal arrays. This article proves a multivariate central limit theorem for a class of randomized orthogonal array sampling designs [Owen (1992a)] as well as for a class of OA-based Latin hypercubes [Tang (1993)]. | |
| dc.description | 89 pages | |
| dc.identifier | https://arxiv.org/abs/0708.0656 | |
| dc.identifier | http://arxiv.org/abs/0708.0656 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/135593 | |
| dc.subject | Statistics Theory | |
| dc.subject | Methodology | |
| dc.title | A multivariate central limit theorem for randomized orthogonal array sampling designs in computer experiments | |
| dc.type | text |