Prediction of growth: A hierarchical Bayesian approach

被引：3
|
作者：
Arjas, E
Liu, LP
Maglaperidze, N
机构：
[1] Univ Helsinki, Rolf Nevanlinna Inst, Helsinki 00014, Finland
[2] Peking Univ, Dept Probabil & Stat, Beijing, Peoples R China
[3] Georgian Stat Assoc, Tbilisi, Georgia
来源：
BIOMETRICAL JOURNAL | 1997年 / 39卷 / 06期
关键词：
growth curve; hierarchical modelling; predictive distribution; Bayesian credible interval;
D O I：
10.1002/bimj.4710390612
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
A nonparametric hierarchical growth curve, model is proposed. Different levels in the model hierarchy are intended to correspond to different sources of variation in an individual's growth. The nonparametric character of the model offers considerable flexibility in fitting the growth curves to empirical data. Here the emphasis is on prediction, and for this purpose the adopted Bayesian inferential approach seems particularly natural and efficient. A Markov chain Carlo method is used to perform the numerical computations. As an illustration of the techniques, we consider the: growth of children, during their first two years.
引用
收藏
页码：741 / 759
页数：19
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