The gradient function as an exploratory goodness-of-fit assessment of the random-effects distribution in mixed models.
The gradient function as an exploratory goodness-of-fit assessment of the random-effects distribution in mixed models.
复制标题
梯度函数作为混合模型中随机效应分布的探索性拟合优度评估。
作者：
G. Verbeke;G. Molenberghs
混合模型中的推断通常基于从预先指定的（通常是参数）分布上整合随机效应获得的边际分布。在本文中，我们提出了所谓的梯度函数作为一个简单的图形探索性诊断工具，以评估是否假设的随机效应分布产生足够的拟合数据，在边际似然。除了拟合模型所需的计算外，该方法不需要任何计算，只要正确指定了以随机效应为条件的结果分布，就可以应用于具有单变量和多变量随机效应的各种混合模型（线性、广义线性、非线性）。在模型设定错误的情况下，梯度函数给出了一个重要的，尽管是非正式的，关于如何在随机效应分布方面改进模型的指示。梯度函数的诊断价值被广泛说明使用一些模拟的例子，以及在分析一个真实的纵向研究与二进制的结果值。
Inference in mixed models is often based on the marginal distribution obtained from integrating out random effects over a pre-specified, often parametric, distribution. In this paper, we present the so-called gradient function as a simple graphical exploratory diagnostic tool to assess whether the assumed random-effects distribution produces an adequate fit to the data, in terms of marginal likelihood. The method does not require any calculations in addition to the computations needed to fit the model, and can be applied to a wide range of mixed models (linear, generalized linear, non-linear), with univariate as well as multivariate random effects, as long as the distribution for the outcomes conditional on the random effects is correctly specified. In case of model misspecification, the gradient function gives an important, albeit informal, indication on how the model can be improved in terms of random-effects distribution. The diagnostic value of the gradient function is extensively illustrated using some simulated examples, as well as in the analysis of a real longitudinal study with binary outcome values.
影响因子：
1.9
作者：
Thomas R. Ten Have;Allen R. Kunselman;Erik P. Pulkstenis;J. Richard Landis
通讯作者：
Thomas R. Ten Have;Allen R. Kunselman;Erik P. Pulkstenis;J. Richard Landis