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Article
2023
Bayesian A-optimal two-phase designs with a single blocking factor in each phase
Bayesian A-optimal two-phase designs with a single blocking factor in each phase
Abstract (English)
Two-phase experiments are widely used in many areas of science (e.g., agriculture, industrial engineering, food processing, etc.). For example, consider a two-phase experiment in plant breeding. Often, the first phase of this experiment is run in a field involving several blocks. The samples obtained from the first phase are then analyzed in several machines (or days, etc.) in a laboratory in the second phase. There might be field-block-to-field-block and machine-to-machine (or day-to-day, etc.) variation. Thus, it is practical to consider these sources of variation as blocking factors. Clearly, there are two possible strategies to analyze this kind of two-phase experiment, i.e., blocks are treated as fixed or random. While there are a few studies regarding fixed block effects, there are still a limited number of studies with random block effects and when information of block effects is uncertain. Hence, it is beneficial to consider a Bayesian approach to design for such an experiment, which is the main goal of this work. In this paper, we construct a design for a two-phase experiment that has a single treatment factor, a single blocking factor in each phase, and a response that can only be observed in the second phase.
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Statistics and computing, 33 (2023), 1, 10.
https://doi.org/10.1007/s11222-022-10126-x.
ISSN: 1573-1375
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Vo-Thanh, N., & Piepho, H.-P. (2023). Bayesian A-optimal two-phase designs with a single blocking factor in each phase. Statistics and computing, 33(1). https://doi.org/10.1007/s11222-022-10126-x
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Sustainable Development Goals
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@article{Vo-Thanh2023,
doi = {10.1007/s11222-022-10126-x},
author = {Vo-Thanh, Nha and Piepho, Hans-Peter},
title = {Bayesian A-optimal two-phase designs with a single blocking factor in each phase},
journal = {Statistics and Computing},
year = {2023},
volume = {33},
number = {1},
}