• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1015-1027
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• 2009

Many problems of confounding and identifiability for polynomial models in an experimental design can be solved using methods of algebraic geometry. The theory of $Gr\ddot{o}bner$ basis is used to characterize the design. In addition, a fractional factorial design can be uniquely represented by a polynomial indicator function. $Gr\ddot{o}bner$ bases and indicator functions are powerful computational tools to deal with ideals of fractions based on each different theoretical aspects. The problem posed here is to give how to move from one representation to the other. For a given fractional factorial design, the indicator function can be computed from the generating equations in the $Gr\ddot{o}bner$ basis. The theory is tested using some fractional factorial designs aided by a modern computational algebra package CoCoA.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1295-1303
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• 2015

In a mixture experiment, one may be interested in estimating not only main effects but also some interactions. Main effects and interactions may be estimated through appropriate designs such as simplex-centroid designs. However, the estimability problems, implied by the sum to one functional relationship among the factors, have strong consequences on the confounding and identifiability of models for such designs. To handle these problems, we address homogeneous polynomial model based on the computational commutative algebra (CCA) instead of using $Scheff{\acute{e}}s$ canonical model which is typically used. The problem posed here is to give how to choose estimable main effects and also some low-degree interactions. The theory is tested using a fraction of simplex-centroid designs aided by a modern computational algebra package CoCoA.