標題: Bayesian prediction analysis for growth curve model using noninformative priors
作者: Shieh, G
Lee, JC
統計學研究所
管理科學系
Institute of Statistics
Department of Management Science
關鍵字: approximations;Metropolis-Hastings;posterior;random coefficient regression;Rao-Blackwellization
公開日期: 1-六月-2002
摘要: We apply a Bayesian approach to the problem of prediction in an unbalanced growth curve model using noninformative priors. Due to the complexity of the model, no analytic forms of the predictive densities are available. We propose both approximations and a prediction-oriented Metropolis-Hastings sampling algorithm for two types of prediction, namely the prediction of future observations for a new subject and the prediction of future values for a partially observed subject. They are illustrated and compared through real data and simulation studies. Two of the approximations compare favorably with the approximation in Fearn (1975, Biometrika, 62, 89-100) and are very comparable to the more accurate Rao-Blackwellization from Metropolis-Hastings sampling algorithm.
URI: http://dx.doi.org/10.1023/A:1022474018976
http://hdl.handle.net/11536/28768
ISSN: 0020-3157
DOI: 10.1023/A:1022474018976
期刊: ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS
Volume: 54
Issue: 2
起始頁: 324
結束頁: 337
顯示於類別:期刊論文


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  1. 000176673400006.pdf