Minimax Distance Designs In 2-Level Factorial-Experiments

    Authors

    P. W. M. John; M. E. Johnson; L. M. Moore;D. Ylvisaker

    Comments

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    Abbreviated Journal Title

    J. Stat. Plan. Infer.

    Keywords

    BAYESIAN DESIGN; COMPUTER EXPERIMENTS; DESIGN OPTIMALITY CRITERIA; 2-LEVEL FRACTIONAL FACTORIAL DESIGN; Statistics & Probability

    Abstract

    A minimax distance criterion was set forth in Johnson et al. (1990) for the purpose of selection among experimental designs. Unlike the usual design criteria such as D-, E- or G-optimality, minimax distance presumes no underlying model and, in turn, is not concerned with the rank of an associated design matrix. In situations where either the model is unknown or it is not possible to run enough experiments to estimate all parameters of an assumed model, this criterion is considered as a viable tool in the task of design selection. This paper deals with the design space associated with n factors, each of which can take two levels. We exhibit minimax distance designs that compare favorably with designs chosen to do well on classical grounds.

    Journal Title

    Journal of Statistical Planning and Inference

    Volume

    44

    Issue/Number

    2

    Publication Date

    1-1-1995

    Document Type

    Article

    Language

    English

    First Page

    249

    Last Page

    263

    WOS Identifier

    WOS:A1995QP79100009

    ISSN

    0378-3758

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