Rank regression for analysis of clustered data: A natural induced smoothing approach

Fu, Liya, Wang, You-Gan, & Bai, Zhidong (2010) Rank regression for analysis of clustered data: A natural induced smoothing approach. Computational Statistics & Data Analysis, 54(4), pp. 1036-1050.

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Abstract

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.

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10 citations in Scopus
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8 citations in Web of Science®

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ID Code: 90448
Item Type: Journal Article
Refereed: Yes
Keywords: failure time model, longitudinal data, linear-models, large-sample, sum, test, misspecification
DOI: 10.1016/j.csda.2009.10.015
ISSN: 0167-9473
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 17 Nov 2015 04:15
Last Modified: 03 Dec 2015 05:55

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