Robustness of Higher Levels Rotatable Designs for Two Factors against Missing Data
Keywords:Robustness Criterion, Missing Data, Four and Five Level Designs, Second Order Rotatability
Experimenters should be aware of the possibility that some of their observations may be unavailable for analysis. This paper considers a criterion that assesses the robustness for missing data when running four and five levels designs in estimating a full second-order polynomial model. The criterion gives the maximum number of runs that can be missing and still allow the remaining runs to estimate a second-order model for four and five levels.
Godolphin, P., & Godolphin, E. (2019). Robustness of crossover trials against subject drop-out- examples of perpetually connected designs. Statistical Methods in Medical Research, 28(3), 788-800.
Godolphin, J. (2019). Connectivity of Incomplete Block Designs. Quality and reliability Engineering International, 35(5), 1299-1287.
Box, G. E., & Draper, N. (1975). Robust designs. Biometrika, 62(2), 347-352.
Herzberg, A., & Andrews, D. (1976). Some considerations in the optimal design of experiments in non-optimal solutions. journal of royal statistical society: series B (Methodological), 38(3), 284-289.
Molenbergs, G., & Kenward, G. (2007). Missing data in clinical studies. Chichester, UK: Wiley.
Rubin, D. (1976). Inference and missing data. Biometrika, 63(3), 581-592.
Tanco, M., & Elizabeth, V. (2013). Robustness of three-level response surface designs against missing data. IIE Transactions, 45(5), 544-553.
Whittinghill, D. (1998). A note on the robustness of Box and Benken designs to the unavailability of data. Metrika, 48(1), 49-52.
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Copyright (c) 2021 Nyakundi Omwando Cornelious, Evans Mbuthi Kilonzo
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