Two More Criteria Equivalent to D-Optimality of Designs

Augmenting supersaturated designs with Bayesian D-optimality

Amethodology is developed to add runs to existing supersaturated designs. The technique uses information from the analysis of the initial experiment to choose the best possible follow-up runs. After analysis of the initial data, factors are classified into one of three groups: primary, secondary, and potential. Runs are added to maximize a Bayesian D-optimality criterion to increase the informa...

On the Choice of Optimality Criteria in Comparing Statistical Designs

Branch - and - Bound Search for Experimental Designs Based on D Optimality and Other Criteria

This article presents a branch-and-bound algorithm that constructs a catalog of all D-optimal n-point designs for specified design region, linear model, and number of observations, n. While the primary design criterion is D optimality, the algorithm may also be used to find designs performing well by other secondary criteria, if a small sacrifice in efficiency as measured by D optimality is acc...

Optimality Aspects of Designs

1. Introduction Careful experimentation is an essential part of any problem of decision-making. An experiment is a device or means of getting an answer to the problem under consideration. In any sphere of scientific research, the general procedure that the experimenter has to adopt is to formulate the hypothesis according to the problem whose answer is to be sought for and then to verify the fo...

Doubly equivalent designs

Mendelsohn and Liang, in J. Combin. Des. 11 (2003), introduced a new class of difference sets and designs: (v, k, [λ1, λ2, . . . , λm])-difference sets and (v, k, [λ1, λ2, . . . , λm])-designs, and mainly discussed designs with a relationship called λ-equivalence. In this paper, we introduce the concepts of a doubly equivalent design as well as a super class. We describe the structure of super ...