Bayesian D-optimal supersaturated designs

We introduce a new class of supersaturated designs using Bayesian D-optimality. The designs generated using this approach can have arbitrary sample sizes, can have any number of blocks of any size, and can incorporate categorical factors with more than two levels. In side by side diagnostic comparisons based on the E(s²)$E(s^2)$ criterion for two-level experiments having even sample size, our designs either match or out-perform the best designs published to date. The generality of the method is illustrated with quality improvement experiment with 15 runs and 20 factors in 3 blocks.     

Toward a Theory of Community Conflict: Factors Influencing the Initiation and Scope of Conflict*

A theory of the structure and process of community conflict is developed around concepts used in earlier works in the field. Then propositions relating to the central elements of community conflict are presented by concentrating on the primary variable: scope. Propositions constructed with other relevant structural variables are introduced. To begin with, procedures used in developing the propositions are discussed; and, then, suggestions for measuring them are given. Finally, implications of the theory in analyses of change are discussed.     

Designing for a Response Transformation Parameter

We investigate D -optimum designs for experiments in which a linear model holds after an unknown power transformation of the univariate response variable. This is a departure from standard D -optimal design in which an appropriate scale for the response is assumed known before data collection. The design problem that we formulate is intrinsically non-linear, requiring characterization of likely parameter values. Several applications are presented to illustrate the importance of recognizing the role of response transformations at the design stage.