Bayesian-inspired minimum contamination designs under a double-pair conditional effect model

Figures & data

Table 2. Regular minimum contamination designs for N = 32.

n	Minimum aberration design
6	$(1,2,4,8,16,31)$
7	$(1,8,16,7,2,4,27)$
8	$(4,16,7,29,1,2,8,11)$
9	$(1,4,7,29,2,8,16,11,19)$
10	$(4,8,7,19,1,2,16,11,29,30)$
11	$(16,11,14,19,1,2,4,8,7,13,21)$
12	$(16,11,13,19,1,2,4,8,7,14,21,22)$
13	$(16,11,13,19,1,2,4,8,7,14,21,22,25)$
14	$(1,4,7,11,2,8,16,13,14,19,21,22,25,26)$
15	$(1,2,4,8,16,32,7,11,13,14,19,22,25,26,28)$
16	$(1,2,4,8,16,7,11,13,14,19,21,22,25,26,28,31)$

Table 1. Regular minimum contamination designs for N = 16.

n	Minimum aberration design
5	$(1,2,4,8,15)$
6	$(1,8,2,4,7,11)$
7	$(1,2,4,8,7,11,13)$
8	$(1,2,4,8,7,11,13,14)$
9	$(2,4,8,3,1,5,9,14,15)$
10	$(1,6,2,8,4,3,5,9,14,15)$
11	$(4,8,5,10,1,2,3,6,9,13,14)$
12	$(2,5,6,10,1,4,8,3,9,13,14,15)$

Table 3. Sixteen-run minimum contamination design with ten factors for the light bulb experiment.

	H	G	I	J	A	B	C	D	E	F
1	1	1	−1	−1	−1	−1	−1	−1	−1	−1
2	1	1	1	−1	1	1	1	−1	−1	−1
3	1	−1	1	1	−1	1	−1	1	−1	−1
4	1	−1	−1	1	1	−1	1	1	−1	−1
5	−1	1	1	1	1	−1	−1	−1	1	−1
6	−1	1	−1	1	−1	1	1	−1	1	−1
7	−1	−1	−1	−1	1	1	−1	1	1	−1
8	−1	−1	1	−1	−1	−1	1	1	1	−1
9	−1	−1	−1	1	1	1	−1	−1	−1	1
10	−1	−1	1	1	−1	−1	1	−1	−1	1
11	−1	1	1	−1	1	−1	−1	1	−1	1
12	−1	1	−1	−1	−1	1	1	1	−1	1
13	1	−1	1	−1	−1	1	−1	−1	1	1
14	1	−1	−1	−1	1	−1	1	−1	1	1
15	1	1	−1	1	−1	−1	−1	1	1	1
16	1	1	1	1	1	1	1	1	1	1