Serroyen Jan, Molenberghs Geert, Verbeke Geert, Davidian Marie
Department of Methodology and Statistics, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, the Netherlands.
Am Stat. 2009 Nov 1;63(4):378-388. doi: 10.1198/tast.2009.07256.
While marginal models, random-effects models, and conditional models are routinely considered to be the three main modeling families for continuous and discrete repeated measures with linear and generalized linear mean structures, respectively, it is less common to consider non-linear models, let alone frame them within the above taxonomy. In the latter situation, indeed, when considered at all, the focus is often exclusively on random-effects models. In this paper, we consider all three families, exemplify their great flexibility and relative ease of use, and apply them to a simple but illustrative set of data on tree circumference growth of orange trees.
虽然边际模型、随机效应模型和条件模型通常分别被视为具有线性和广义线性均值结构的连续和离散重复测量的三个主要建模类别，但考虑非线性模型的情况较少，更不用说将它们纳入上述分类法了。在后一种情况下，实际上，即使有所考虑，重点通常也完全放在随机效应模型上。在本文中，我们考虑了所有这三个类别，举例说明了它们极大的灵活性和相对易用性，并将它们应用于一组关于橙子树周长增长的简单但具有说明性的数据。
