MULTILEVEL TIME-SERIES MODELS WITH APPLICATIONS TO REPEATED-MEASURES DATA
MULTILEVEL TIME-SERIES MODELS WITH APPLICATIONS TO REPEATED-MEASURES DATA
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DOI：
10.1002/sim.4780131605
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发表时间：
1994-08-30
影响因子：
2
通讯作者：
RASBASH, J
中科院分区：
文献类型：
--
作者：
GOLDSTEIN, H;HEALY, MJR;RASBASH, J
使用两水平随机系数模型可以有效地进行重复测量数据的分析。标准假设是个体内（水平1）残差不相关。在某些情况下，特别是在测量时间接近的情况下，这可能是不合理的，还应该对这种额外的相关性结构进行建模。这种数据的时间序列模型的建议，其中包括一个标准的多层次模型的重复测量数据增强的自相关模型的水平1残差。一阶和二阶自回归模型被认为是详细的，连同季节性成分。离散和连续的时间被认为是如何自相关参数本身可以构造在进一步的解释变量。模型拟合到由儿童重复身高测量组成的数据集。
The analysis of repeated measures data can be conducted efficiently using a two-level random coefficients model. A standard assumption is that the within-individual (level 1) residuals are uncorrelated. In some cases, especially where measurements are made close together in time, this may not be reasonable and this additional correlation structure should also be modelled. A time series model for such data is proposed which consists of a standard multilevel model for repeated measures data augmented by an autocorrelation model for the level 1 residuals. First- and second-order autoregressive models are considered in detail, together with a seasonal component. Both discrete and continuous time are considered and it is shown how the autocorrelation parameters can themselves be structured in terms of further explanatory variables. The models are fitted to a data set consisting of repeated height measurements on children.