Factorial hypercube designs for spatial correlation regression

The problem of generating a good experimental design for spatial correlation regression is studied in this paper. The quality of fit generated by random designs, Latin hypercube designs and factorial designs is studied for a particular response surface that arises in inkjet printhead design. These studies indicate that the quality of fit generated by spatial correlation models is highly dependent on the choice of design. A design strategy that we call 'factorial hypercubes' is introduced as a new method. This method can be thought of as an example of a more general class of hybrid designs. The quality of fit generated by these designs is compared with those of other methods. These comparisons indicate a better fit and less numerical problems with factorial hypercubes.