Abstract
Consider the design problem for the approximately linear model with serially correlated errors. The correlated structure is the qth degree moving average process, MA(q), especially for q = 1, 2. The optimal design is derived by using Bayesian approach. The Bayesian designs derived with various priors are compared with the classical designs with respect to some specific correlated structures. The results show that any prior knowledge about the sign of the MA(q) process parameters leads to designs that are considerately more efficient than the classical ones based on homoscedastic assumptions.
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This work was partially supported by NSFC Grant (10671129), E-Institutes of Shanghai Municipal Education Commission (E03004), Special Funds for Doctoral Authorities of Education Ministry of China (20060270002), Science and Technology Commission of Shanghai Municipality grant 075105118, and Shanghai Leading Academic Discipline Project (5/Z/206).
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Yue, RX., Zhou, XD. Bayesian robust designs for linear models with possible bias and correlated errors. Metrika 71, 1–15 (2010). https://doi.org/10.1007/s00184-008-0197-0
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DOI: https://doi.org/10.1007/s00184-008-0197-0