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All Journal JASDS: Journal of Applied Statistics and Data Science
Nwanya, Julius
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Computer Science & IT Mathematics

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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 Vol. 3 No. 1 (2026): Journal of Applied Statistics and Data Science
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jasds.2026.003.01.3

Abstract

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.
Co-Authors Efeizomor, Rita Mbachu, Hope