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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Split-Plot and Multi-Stratum Designs for Statistical Inference

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Author(s):
Trinca, Luzia A. [1] ; Gilmour, Steven G. [2]
Total Authors: 2
Affiliation:
[1] Sao Paulo State Univ, Dept Biostat, Botucatu, SP - Brazil
[2] Kings Coll London, Dept Math, London - England
Total Affiliations: 2
Document type: Journal article
Source: TECHNOMETRICS; v. 59, n. 4, p. 446-457, 2017.
Web of Science Citations: 4
Abstract

It is increasingly recognized that many industrial and engineering experiments use split-plot or other multi-stratum structures. Much recent work has concentrated on finding optimum, or near-optimum, designs for estimating the fixed effects parameters in multi-stratum designs. However, often inference, such as hypothesis testing or interval estimation, will also be required and for inference to be unbiased in the presence of model uncertainty requires pure error estimates of the variance components. Most optimal designs provide few, if any, pure error degrees of freedom. Gilmour and Trinca (2012) introduced design optimality criteria for inference in the context of completely randomized and block designs. Here these criteria are used stratum-by-stratum to obtain multi-stratum designs. It is shown that these designs have better properties for performing inference than standard optimum designs. Compound criteria, which combine the inference criteria with traditional point estimation criteria, are also used and the designs obtained are shown to compromise between point estimation and inference. Designs are obtained for two real split-plot experiments and an illustrative split-split-plot structure. Supplementary materials for this article are available online. (AU)

FAPESP's process: 14/01818-0 - Robust optimum design of experiments
Grantee:Luzia Aparecida Trinca
Support type: Regular Research Grants