Estimation of Interclass Correlation from Familial Data
Estimation of Interclass Correlation from Familial Data
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DOI：
10.2307/2347026
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发表时间：
1977-06
影响因子：
1.6
通讯作者：
B. Rosner;A. Donner;C. Hennekens
中科院分区：
文献类型：
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作者：
B. Rosner;A. Donner;C. Hennekens
在对家族数据的分析中，为了量化亲子相似性的程度，使用了几种不同的类间相关性估计，成对，同胞平均值和随机同胞方法。我们比较这些方法，并提出了一种新的估计，集成估计。对于每个家庭有固定数量的兄弟姐妹的情况下，成对估计被证明是等价的最大似然估计。当每个家庭的兄弟姐妹的数量是可变的，成对和集成估计的Monte Carlo模拟显示，由于其较小的均方误差是优选的。当同胞-同胞相关性较低时，成对估计更有效，而在高值时，集合估计更有效。
In the analysis of familial data in order to quantify the degree of parent–child resemblance several different estimators of interclass correlation, the pairwise, sib‐mean and random‐sib methods, have been used. We compare these methods and propose a new estimator, the ensemble estimator. For the case where there are a fixed number of siblings per family, the pairwise estimator is shown to be equivalent to the maximum likelihood estimator. When there are a variable number of siblings per family, the pairwise and ensemble estimators are shown by Monte Carlo simulation to be preferable due to their smaller mean square errors. When the sib‐sib correlation is low, the pairwise estimator is more effective whereas at high values the ensemble estimator is more effective.