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.

View at publisher


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.

Impact and interest:

11 citations in Scopus
9 citations in Web of Science®
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

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

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page