摘要:

Fractional factorial, Plackett-Burman, and other multifactor designs are often effective in practice due to factor sparsity. That is, just a few of the many factors studied will have major effects. In those active factors, these designs can have high resolution. We have previously developed a Bayesian method based on the idea of model discrimination that uncovers the active factors. Sometimes, the results of a fractional experiment are ambiguous due to confounding among the possible effects, and more than one model may be consistent with the data. Within the Bayesian construct, we have developed a method for designing a follow-up experiment to resolve this ambiguity. The idea is to choose runs that allow maximum discrimination among the plausible models. This method is more general than methods that algebraically decouple aliased interactions and more appropriate than optimal design methods that require specification of a single model. The method is illustrated through examples of fractional experiments.

ISSN:0040-1706    

DOI:10.1080/00401706.1996.10484538

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

ISSN:0040-1706    

DOI:10.1080/00401706.1996.10484539

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

ISSN:0040-1706    

DOI:10.1080/00401706.1996.10484540

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

ISSN:0040-1706    

DOI:10.1080/00401706.1996.10484541

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

ISSN:0040-1706    

DOI:10.1080/00401706.1996.10484542

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

ISSN:0040-1706    

DOI:10.1080/00401706.1996.10484543

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

ISSN:0040-1706    

DOI:10.1080/00401706.1996.10484544

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

摘要:

When experiments are carried out over a period of time, the response may be subject to time trends. We use an algorithm for exact optimum designs to construct a series of designs resistant to linear and quadratic trend. Designs considered include the allocation of simple treatments, multifactor designs in qualitative or quantitative factors, and response surface designs. The investigation is extended from consideration of linear ordering in time to include designs with several trials at each time point, designs in which several trials are spread over only one out of three shifts per day, and designs in which there are more time points than experiments. Comparisons with designs in the absence of trend show that surprisingly little information is lost by designing for protection against a potential trend. The BT algorithm for obtaining these designs is outlined.

ISSN:0040-1706    

DOI:10.1080/00401706.1996.10484545

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

摘要:

Fractional two-level factorial designs are often used in the early stages of an investigation to screen for important factors. Traditionally, 2n-kfractional factorial designs of resolution III, IV, or V have been used for this purpose. When the investigator is able to specify the set of nonnegligible factorial effects, it is sometimes possible to obtain an orthogonal design with fewer runs than a standard textbook design by searching within a wider class of designs called parallel-flats designs. The run sizes in this class of designs do not necessarily need to be powers of 2. We discuss an algorithm for constructing orthogonal parallel-flats designs to meet user specifications. Several examples illustrate the use of the algorithm.

ISSN:0040-1706    

DOI:10.1080/00401706.1996.10484546

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

摘要:

Dispersion experiments examine the effects of control factors on dispersion introduced by error factors. Error factors can be controlled in the laboratory but cannot be controlled when a product is used. For dispersion experiments, Taguchi used direct product designs in which control-factor levels are set by one orthogonal array, error-factor levels are set by another orthogonal array, and, for each combination of control-factor and error-factor levels, an observation is taken. In contrast, compound designs, which include product designs as special cases, share several of the attractive properties of Taguchi's designs, but they are often smaller for a given strength, or stronger for a given size, where strength refers to strength of the orthogonal array. This article discusses several useful compound designs for dispersion experiments and several general methods for constructing such designs. In particular, compound designs can be built from existing tabulations of confounded factorial designs.

ISSN:0040-1706    

DOI:10.1080/00401706.1996.10484547

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor