Analysis of variance designs for model output

✍ Scribed by Michiel J.W. Jansen

• Publisher: Elsevier Science
• Year: 1999
• Tongue: English
• Weight: 533 KB
• Volume: 117
• Category: Article
• ISSN: 0010-4655
• DOI: 10.1016/s0010-4655(98)00154-4

✦ Synopsis

A scalar model output Y is assumed to depend deterministically on a set of stochastically independent input vectors of different dimensions. The composition of the variance of Y is considered; variance components of particular relevance for uncertainty analysis are identified. Several analysis of variance designs for estimation of these variance components are discussed. Classical normal-model theory can suggest optimal designs. The designs can be implemented with various sampling methods: ordinary random sampling, latin hypercube sampling and scrambled quasi-random sampling. Some combinations of design and sampling method are compared in two small-scale numerical experiments. @ 1999 Elsevier Science B.V.