Abstract
This study constructs robust split-plot central composite designs against missing pairs of observations. Split-plot central composite designs (CCD) consist of factorial (f), whole-plot axial (α), subplot axial (β), and center (c) points. A loss function in terms of determinant (D) criterion was formulated based on two different configurations of the factorial and axial parts; losses due to missing pairs of observations of these different categories of points were investigated. Robust split-plot central composite designs against missing pairs of observations were then developed under each of the two configurations. It was observed that the losses, L ff , L ββ , and L fβ , due respectively, to missing pairs of observations of the factorial, subplot axial, and, factorial and subplot axial points, were higher than the losses due to missing pairs of observations of the whole-plot axial and center points given by L αα and L cc respectively. Thus the factorial (f) and the subplot axial (β) points were found to be the most influential points in these designs while the whole-plot axial (α) and the center (c) points were less influential. This work has therefore identified and properly classified the losses due to missing design points in the split-plot CCD portions. In this way, the practitioner can avoid the experimental points having less influence from the full CCD experiments and this could lead to a possible increase in design efficiency. Keywords : Robustness, Split-plot Central Composite Designs, Missing observations, loss function
Highlights
Response Surface Methodology (RSM) is an area of experimental design which consists of a group of mathematical and statistical techniques used in the development of an adequate functional relationship between a response of interest, y, and a number of associated control variables denoted by
A number of researchers have studied the loss due to a set of missing observations over a range of α value associated with completely randomized central composite designs (CCD) using the loss function introduced by Akhtar and Prescott (1986)
The computed maximum losses (, and ) due to missing pairs of observations of factorial(f), whole-plot axial( ), subplot axial( ), and center(c) points are presented in the tables below for each of the selected split-plot CCDs and under each of YAKUBU, Y; CHUKWU, A U
Summary
Response Surface Methodology (RSM) is an area of experimental design which consists of a group of mathematical and statistical techniques used in the development of an adequate functional relationship between a response of interest, y, and a number of associated control (or input) variables denoted by. A number of researchers have studied the loss due to a set of missing observations over a range of α value associated with completely randomized central composite designs (CCD) using the loss function introduced by Akhtar and Prescott (1986). Minimaxloss designs that are robust to sets of missing observations have effectively emerged Much of these studies consider the second-order central composite designs differing in number of control variables and configurations of the factorial, axial and center portions. Some of such results include Akhtar and. The objective of this study is to assess the loss due to missing pairs of observations in split-plot central composite designs and to construct split-plot central composite designs that are robust against these missing pairs of observations
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