Wang You-Gan, Zhao Yudong
CSIRO Mathematical and Information Sciences, CSIRO Long Pocket Laboratories, 120 Meiers Road, Indooroopilly, Queensland 4068, Australia.
Biometrics. 2008 Mar;64(1):39-45. doi: 10.1111/j.1541-0420.2007.00842.x. Epub 2007 Jun 30.
We consider ranked-based regression models for clustered data analysis. A weighted Wilcoxon rank method is proposed to take account of within-cluster correlations and varying cluster sizes. The asymptotic normality of the resulting estimators is established. A method to estimate covariance of the estimators is also given, which can bypass estimation of the density function. Simulation studies are carried out to compare different estimators for a number of scenarios on the correlation structure, presence/absence of outliers and different correlation values. The proposed methods appear to perform well, in particular, the one incorporating the correlation in the weighting achieves the highest efficiency and robustness against misspecification of correlation structure and outliers. A real example is provided for illustration.
我们考虑用于聚类数据分析的基于秩的回归模型。提出了一种加权Wilcoxon秩方法，以考虑聚类内相关性和不同的聚类大小。建立了所得估计量的渐近正态性。还给出了一种估计估计量协方差的方法，该方法可以绕过密度函数的估计。进行了模拟研究，以比较在相关结构、异常值的存在与否以及不同相关值的多种情况下的不同估计量。所提出的方法表现良好，特别是在加权中纳入相关性的方法在针对相关结构和异常值的错误指定方面实现了最高效率和稳健性。提供了一个实际例子进行说明。
