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A systematic construction of compromise designs under baseline parameterization
- Source :
- Journal of Statistical Planning and Inference. 219:33-42
- Publication Year :
- 2022
- Publisher :
- Elsevier BV, 2022.
-
Abstract
- Karunanayaka and Tang (2017) introduced a class of compromise designs for estimating main effects under the baseline parameterization. Their approach is to add some runs to the basic one-factor-at-a-time design, and its implementation requires computer search except for the case of adding one run where a theoretical result is available. The reliance on computer search only allowed them to find the limited results from adding up to four runs to the basic one-factor-at-a-time design. In this paper, we provide a systematic construction of compromise designs, without computer search and without restriction on the run size and the number of factors. Closed-form expressions of the bias and efficiency criteria are obtained for the new designs. Both theoretical and empirical studies show that our designs outperform those of Karunanayaka and Tang (2017) saving small-sized problems.
- Subjects :
- Statistics and Probability
0303 health sciences
Class (computer programming)
Mathematical optimization
Applied Mathematics
Compromise
media_common.quotation_subject
01 natural sciences
010104 statistics & probability
03 medical and health sciences
Empirical research
0101 mathematics
Statistics, Probability and Uncertainty
Baseline (configuration management)
Computer search
030304 developmental biology
media_common
Mathematics
Subjects
Details
- ISSN :
- 03783758
- Volume :
- 219
- Database :
- OpenAIRE
- Journal :
- Journal of Statistical Planning and Inference
- Accession number :
- edsair.doi...........7c95f5c56445eeb3d4df3aacf9b999a8