Quantile regression for longitudinal data with a working correlation model

Fu, Liya and Wang, You-Gan (2012) Quantile regression for longitudinal data with a working correlation model. Computational Statistics and Data Analysis, 56 8: 2526-2538. doi:10.1016/j.csda.2012.02.005

Author Fu, Liya
Wang, You-Gan
Title Quantile regression for longitudinal data with a working correlation model
Journal name Computational Statistics and Data Analysis   Check publisher's open access policy
ISSN 0167-9473
Publication date 2012-08
Sub-type Article (original research)
DOI 10.1016/j.csda.2012.02.005
Volume 56
Issue 8
Start page 2526
End page 2538
Total pages 13
Place of publication Amsterdam, The Netherlands
Publisher Elsevier
Collection year 2013
Language eng
Formatted abstract
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.
Keyword Covariance estimate
Unbiased estimating functions
Exchangeable correlation structure
Independence working model
Induced smoothing method
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Received 27 September 2011; Received in revised form 2 February 2012; Accepted 5 February 2012; Available online 11 February 2012

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Official 2013 Collection
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Citation counts: TR Web of Science Citation Count  Cited 10 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 12 times in Scopus Article | Citations
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