Fitting Second-order Models to Mixed Two-level and Four-level Factorial Designs: Is There an Easier Procedure?

Saturated Second-Order Two-Level Designs: An Empirical Approach

Computer methods are used to explore saturated designs which provide for optimal estimation of main e ects and interactions between two-level factors. A series of designs is thus discovered, related to a known series but better for k > 6. Also, a relationship is discovered between two di erent classes designs which should be fruitful for future research.

Bayesian-inspired mixed two- and four-level designs

Motivated by a Bayesian framework, we propose a new minimum aberration type criterion for designing experiments with twoand four-level factors. The Bayesian approach helps in overcoming the ad hoc nature of effect ordering in the existing minimum aberration type criteria. Moreover, the approach is also capable of distinguishing between qualitative and quantitative factors. Numerous examples are...

Construction of Efficient Fractional Factorial Mixed-Level Designs

Optimal Foldover Plans for Two-Level Fractional Factorial Designs

A commonly used follow-up experiment strategy involves the use of a foldover design by reversing the signs of one or more columns of the initial design. DeŽ ning a foldover plan as the collection of columns whose signs are to be reversed in the foldover design, this article answers the following question: Given a 2kp design with k factors and p generators, what is its optimal foldover plan? We ...

Optimal Designs for Two-level Factorial Experiments with Binary Response

We consider the problem of obtaining locally D-optimal designs for factorial experiments with qualitative factors at two levels each with binary response. Our focus is primarily on the 2 experiment. In this paper, we derive analytic results for some special cases and indicate how to handle the general case. The performance of the uniform design in examined and we show that this design is highly...

Bayesian - inspired minimum aberration two - and four - level designs

Motivated by a Bayesian framework, we propose a new minimum aberration-type criterion for designing experiments with twoand four-level factors. The Bayesian approach helps in overcoming the ad hoc nature of effect ordering in the existing minimum aberration-type criteria. The approach is also capable of distinguishing between qualitative and quantitative factors. Numerous examples are given to ...