Quantile regression for longitudinal data with a working correlation model

Fu, Liya & Wang, You-Gan (2012) Quantile regression for longitudinal data with a working correlation model. Computational Statistics & Data Analysis, 56(8), pp. 2526-2538.

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This paper proposes a linear quantile regression analysis method for longitudinal data that combines the between- and within-subject estimating functions, which incorporates the correlations between repeated measurements. Therefore, the proposed method results in more efficient parameter estimation relative to the estimating functions based on an independence working model. To reduce computational burdens, the induced smoothing method is introduced to obtain parameter estimates and their variances. Under some regularity conditions, the estimators derived by the induced smoothing method are consistent and have asymptotically normal distributions. A number of simulation studies are carried out to evaluate the performance of the proposed method. The results indicate that the efficiency gain for the proposed method is substantial especially when strong within correlations exist. Finally, a dataset from the audiology growth research is used to illustrate the proposed methodology.

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11 citations in Scopus
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10 citations in Web of Science®

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ID Code: 90437
Item Type: Journal Article
Refereed: Yes
Keywords: Covariance estimate, Unbiased estimating functions, Exchangeable, correlation structure, Independence working model, Induced smoothing, method, clustered data, errors, estimators, time
DOI: 10.1016/j.csda.2012.02.005
ISSN: 0167-9473
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 17 Nov 2015 04:44
Last Modified: 03 Dec 2015 04:56

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