Simulation-based Bayesian inferences for two-variance components linear models |
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| Institution: | 1. Swiss Tropical Institute, Socinstrasse 57, Basle, Switzerland;2. Department of Mathematics, Imperial College, 180 Queen''s Gate, London 7 2BZ, UK;1. State Key Laboratory of Catalysis, Dalian National Laboratory for Clean Energy, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, Liaoning, China;2. College of Energy, University of Chinese Academy of Sciences, Beijing 100039, China;3. Center for Advanced Mössbauer Spectroscopy, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, Liaoning, China;1. Department of Ophthalmology, Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland;2. Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland;3. Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland;1. Department of Infrastructure Engineering, University of Innsbruck, Technikerstrasse 13, 6020 Innsbruck, Austria;2. Department of Geography, Hebrew University of Jerusalem, Mt. Scopus, 919051 Jerusalem, Israel;3. Department of the Built Environment, Eindhoven University of Technology, 5600 MB Eindhoven, the Netherlands;4. Department of Air Transportation Management, Nanjing University of Aeronautics and Astronautics, 211106 Nanjing, China;1. Guangdong Provincial Key Laboratory of Eco-circular Agriculture, South China Agricultural University, Guangzhou 510642, China;2. Key Laboratory of Agro-Environment in the Tropics, Ministry of Agriculture and Rural Affairs, South China Agricultural University, Guangzhou 510642, China;3. College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China |
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| Abstract: | We present a Bayesian analysis of variance component models via simulation. In particular, we study the 2-component hierarchical design model under balanced and unbalanced experiments. Also, we consider 2-factor additive random effect models and mixed models in a cross-classified design. We assess the sensitivity of inference to the choice of prior by a sampling/resampling technique. Finally, attention is given to non-normal error distributions such as the heavy-tailed t distribution. |
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