To identify influential effects in unreplicated (possibly fractionated) factorial experiments, the effect-sparsity assumption (Box and Meyer (1986), Technometrics 28. 11-18) has been adopted in many studies. Although this assumption has been traditionally used for outlier-detecting problems, it may not be suitable to describe the effects from factorial experiments. In this research, we examine the effect-sparsity approach and propose empirical Bayes methods relaxing this assumption. The study also examines the identification of influential effects based on information about the design structure such as the alias relationships, design resolution, and sizes of interactions. A simulation study, based primarily on the criterion of reducing experimental cost of misidentifying factors, has been performed to compare different methods. The results show that when the number of factors is large and when the factorial experiment is highly fractionated, the incorporation of information about the design structure into the analysis reduces the cost in a screening experiment compared to methods not considering design structure. / Source: Dissertation Abstracts International, Volume: 55-04, Section: B, page: 1504. / Major Professor: Duane A. Meeter. / Thesis (Ph.D.)--The Florida State University, 1994.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_77159 |
| Contributors | Chen, Ching-Hsiang., Florida State University |
| Source Sets | Florida State University |
| Language | English |
| Detected Language | English |
| Type | Text |
| Format | 121 p. |
| Rights | On campus use only. |
| Relation | Dissertation Abstracts International |
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