Designs for experiments with hard-to-set factors
What is it about?
Many experiments involve assessing the effects of several inputs (factors) on one or more outputs (responses). Good design involves choosing to run combinations of factor levels which will make the data analysis as informative as possible. Much recent work has developed designs for situations in which some of the factors have levels which are harder to set than others, so they are varied less often. Most of this has emphasised estimation. This paper extends this work to consider data analysis for carrying out inferences such as hypothesis tests and confidence intervals.
Why is it important?
This paper allows designs to be better tailored to the data analysis which will actually be performed.
The following have contributed to this page: Steven Gilmour