Rank regression for analysis of clustered data: A natural induced smoothing approach
Rank regression for analysis of clustered data: A natural induced smoothing approach
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用于分析聚类数据的排名回归：一种自然诱导平滑方法
DOI：
10.1016/j.csda.2009.10.015
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发表时间：
2010-04-01
影响因子：
1.8
通讯作者：
Bai, Zhidong
中科院分区：
文献类型：
--
作者：
Fu, Liya;Wang, You-Gan;Bai, Zhidong
本文研究了秩回归在聚类数据分析中的应用，并讨论了求参数估计量的渐近协方差矩阵的诱导平滑方法。证明了导出的估计函数是渐近无偏的，所得的估计量是强相合且渐近正态的。诱导平滑方法提供了一种有效的方法来获得渐近协方差矩阵之间和集群内的估计和组合估计，以考虑集群内的相关性。我们还进行了广泛的模拟研究来评估不同估计器的性能。与现有方法相比，本文方法在计算速度和数值结果稳定性方面都有显著提高。我们将所提出的方法应用于随机临床试验的数据集。皇冠版权所有（C）2009年出版的爱思唯尔B.V.保留所有权利。
We consider rank regression for clustered data analysis and investigate the induced smoothing method for obtaining the asymptotic covariance matrices of the parameter estimators. We prove that the induced estimating functions are asymptotically unbiased and the resulting estimators are strongly consistent and asymptotically normal. The induced smoothing approach provides an effective way for obtaining asymptotic covariance matrices for between- and within-cluster estimators and for a combined estimator to take account of within-cluster correlations. We also carry out extensive simulation studies to assess the performance of different estimators. The proposed methodology is substantially Much faster in computation and more stable in numerical results than the existing methods. We apply the proposed methodology to a dataset from a randomized clinical trial. Crown Copyright (C) 2009 Published by Elsevier B.V. All rights reserved.