Some results on two-level factorial designs with dependent observations |
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| Institution: | 1. School of Mathematics and Statistics, University of Sheffield, Sheffield S3 7RH, UK;2. Department of Statistics, Massey University, Palmerston North, New Zealand;3. Department of Mathematics, University of Queensland, Brisbane, Qld 4072, Australia;1. College of Food Science, Southwest University, Chongqing, 400715, PR China;2. Chongqing Engineering Research for Regional Foods, Chongqing, 400715, PR China;1. Chemical Safety Division, National Institute of Agricultural Science, Rural Development Administration, Wanju 55365, Republic of Korea;2. Division of Applied Life Science (BK21 Plus), Institute of Agriculture and Life Science (IALS), Gyeongsang National University, Jinju 52727, Republic of Korea;1. Department of Chemistry, Faculty of Sciences, Universidad de Burgos, Plaza Misael Bañuelos s/n, 09001, Burgos, Spain;2. Department of Mathematics and Computation, Faculty of Sciences, Universidad de Burgos, Plaza Misael Bañuelos s/n, 09001, Burgos, Spain |
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| Abstract: | There are many situations in which observations in factorial experiments may be dependent. When this is so, run orders are needed that result in efficient estimates of contrasts. The Cheng and Steinberg reverse foldover algorithm, which gives a maximal number of level changes, is known to produce very efficient main-effects two-level designs using the D-criterion, but less is known about other designs, models and criteria. We present some further theoretical results, and give another statistic of importance in predicting efficiency under strong dependence. The theory is illustrated using some 16-run designs. |
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