 Replication in random translation designsTimothy W. Waite Statistics & Probability Letters, 2024, vol. 215, issue C Abstract: Replication is a commonly recommended feature of experimental designs. However, its impact in model-robust design is relatively under-explored; indeed, replication is impossible within the current formulation of random translation designs, which were introduced recently for model-robust prediction. Here we extend the framework of random translation designs to allow replication, and quantify the resulting performance impact. The extension permits a simplification of our earlier heuristic for constructing random translation strategies from a traditional V-optimal design. Namely, in the previous formulation any replicates of the V-optimal design first had to be split up before a random translation can be applied to the design points. With the new framework we can instead preserve the replicates instead if we so wish. Surprisingly, we find that in low-dimensional problems it is often substantially more efficient to continue to split replicates, while in high-dimensional problems it can be substantially better to retain replicates. Keywords: Optimal design; Robustness; Minimax design (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:215:y:2024:i:c:s0167715224001986 Ordering information: This journal article can be ordered from DOI: 10.1016/j.spl.2024.110229 Access Statistics for this article Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul More articles in Statistics & Probability Letters from Elsevier |
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