Appropriate covariance-specification via penalties for penalized splines in mixed models for longitudinal data
Appropriate covariance-specification via penalties for penalized splines in mixed models for longitudinal data
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DOI：
10.1214/10-ejs583
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发表时间：
2010-01-01
影响因子：
1.1
通讯作者：
Currie, Iain D.
中科院分区：
文献类型：
--
作者：
Djeundje, Viani A. B.;Currie, Iain D.
一个流行的方法来平滑模型的纵向数据是表示模型作为一个混合模型，因为这往往会导致立即模型拟合与标准程序。当截断多项式用作平滑的基础时，这种方法特别有吸引力，因为混合模型表示几乎是立即的。我们表明，这种方法可能会导致一个严重的偏差估计的整体人口效应和置信区间的不良属性。我们使用惩罚调查的替代方法，无论是B样条或截断多项式基地，并表明这种新的方法不会遭受同样的缺陷。我们的模型定义在B样条或截断多项式与适当的惩罚，但可以表示为混合模型;这也提供了访问与标准程序拟合。我们说明了我们的方法与两个数据集的分析：（a）平衡的数据seton加拿大的天气和（B）一个不平衡的数据集上的儿童的成长。
A popular approach to smooth models for longitudinal data is to express the model as a mixed model, since this often leads to immediate model fitting with standard procedures. This approach is particularly appealing when truncated polynomials are used as a basis for the smoothing, as the mixed model representation is almost immediate. We show that this approach can lead to a severely biased estimate of the overall population effect and to confidence intervals with undesirable properties. We use penalization to investigate an alternative approach with either B-spline or truncated polynomial bases and show that this new approach does not suffer from the same defects. Our models are defined in terms of B-splines or truncated polynomials with appropriate penalties, but can be expressed as mixed models; this also gives access to fitting with standard procedures. We illustrate our methods with an analysis of two datasets: (a) a balanced data seton Canadian weather and (b) an unbalanced dataset on the growth of children.