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Bayesian robust designs for linear models with possible bias and correlated errors |
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| Authors: | Rong-Xian Yue Xiao-Dong Zhou |
| Affiliation: | 1. Department of Mathematics, Shanghai Normal University, Shanghai, China 2. Division of Scientific Computation of E-Institute of Shanghai Universities, Shanghai, China 3. Scientific Computing Key Laboratory of Shanghai Universities, 200234, Shanghai, China 4. Business Information Management School, Shanghai Institute of Foreign Trade, 201620, Shanghai, China |
| 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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