A PARAMETRIC FAMILY OF CORRELATION STRUCTURES FOR THE ANALYSIS OF LONGITUDINAL DATA
A PARAMETRIC FAMILY OF CORRELATION STRUCTURES FOR THE ANALYSIS OF LONGITUDINAL DATA
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DOI：
10.2307/2532340
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发表时间：
1992-09-01
期刊：
影响因子：
1.9
通讯作者：
ROSNER, B
中科院分区：
文献类型：
--
作者：
MUNOZ, A;CAREY, V;ROSNER, B
在流行病学环境中，我们经常面临着众多的短时间序列，并期望一个简约的参数化的相关结构，以优化效率的估计过程。我们提出了一个阻尼指数相关结构建模多元高斯结果。由s个时间单位分隔的两个观测之间的相关性被建模为伽马（s-theta），其中伽马是由一个s单位分隔的元素之间的相关性，并且theta是阻尼参数。对于（θ = 0），（θ = 1）和（θ->无穷大），得到了复合对称过程，一阶自回归过程和一阶移动平均过程的相关结构.虽然AR（2）相关结构以及随机效应和AR（1）误差的组合不是所提出的参数族的特殊情况，但对于短时间序列，这些结构可以在该族内很好地近似。最大似然法参数估计和解释的中间模型（0 <θ < 1）的背景下，在荷兰的成年人的肺功能和T细胞亚群的同性恋男性感染人类免疫缺陷病毒I型的建模进行了讨论。
In epidemiological settings, we are often faced with numerous short time series, and a parsimonious parametrization of the correlation structure is desired in order to optimize the efficiency of the estimation procedure. We propose a damped exponential correlation structure for modeling multivariate Gaussian outcomes. The correlation between two observations separated by s units of time is modeled as gamma(s-theta) where gamma is the correlation between elements separated by one s-unit, and theta is a damping parameter. For (theta = 0), (theta = 1), and (theta --> infinity), the correlation structures of compound symmetry, first-order autoregressive, and first-order moving average processes are obtained. Although the AR(2) dependency structure, and the combination of random effects and AR(1) errors are not special cases of the proposed parametric family, these structures can be well approximated within the family for short time series. Maximum likelihood methods for parameter estimation and interpretations of intermediate models (0 < theta < 1) are discussed in the context of modeling pulmonary function in an adult population in The Netherlands and T-cell subsets in homosexual men infected with human immunodeficiency virus Type I.