Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/278053 
Year of Publication: 
2023
Citation: 
[Journal:] Pakistan Journal of Commerce and Social Sciences (PJCSS) [ISSN:] 2309-8619 [Volume:] 17 [Issue:] 2 [Year:] 2023 [Pages:] 288-312
Publisher: 
Johar Education Society, Pakistan (JESPK), Lahore
Abstract: 
Response surface designs under restricted randomization or Split-plot response surface designs are often used in agriculture experiments and in industrial experiments due to existence of one or more factors that can't change their levels easily some factors need to estimate more precisely. The motive of this paper is to prevail prediction capability of a particular class of split-plot response surface designs, known as Central Composite Designs by Vining, Kowalski and Montgomery (VKM CCDs) when one observation of any category is missed. Both numerical and graphical methods, based on scaled prediction variance (SPV) are applied. Robustness of above class of designs against one missing observation is investigated relative to G-efficiency and Minimax loss designs are proposed. The prediction capability is computed by graphical methods such as 3D Variance Dispersion Graph (3D-VDG), Fraction of Design Space (FDS) plots and contour plots for extraordinary efficiency standards are used to look at the impact of lacking observations.
Subjects: 
Prediction Capability
G-efficiency
Scaled Prediction Variance
Fraction of Design space and Variance Dispersion Graph
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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