The factor aliased effect number pattern and its application in experimental planning

AbstractOptimality criteria are usually used to choose fractional factorial designs in applications. Within an optimal design, the effects of factors assigned to different columns may be estimated with different precisions. Among factors to be investigated in an experiment, the user often has prior information on their relative importance. Thus, it is beneficial to assign most important factors to columns enabling most precise estimation. In this paper, we introduce a criterion to rank the columns of a regular design and use the criterion to GMC designs accordingly. We study the mathematical properties of the new ranking practice and provide concrete guidance on assigning factors in some GMC designs. The Canadian Journal of Statistics 41: 540–555; 2013 © 2013 Statistical Society of Canada

The factor aliased effect number pattern and its application in experimental planning Journal Articles