Latin Hypercube Design Under Second and Third-Order Models: A Prediction Variance Approach - Nwanya, Julius; Efeizomor, Rita; Mbachu, Hope (Journal of Applied Statistics and Data Science, 2026)
Prediction variance describes the error involved with making a prediction using a response surface model. This study examines the prediction variance performance of Latin Hypercube Designs (LHDs) within second- and third-order response surface models. G-optimality, I-optimality criteria, and Fraction of Design Space (FDS) plots were employed to assess the predictive capabilities and accuracy of LHDs. The findings reveal that LHDs perform better under third-order models when evaluated using the G-optimality criterion, while under the I-optimality criterion, LHDs perform better in second-order models. The FDS plots further indicate that as the number of factors increases, the prediction errors across models become approximately similar.