Smoothing combined estimating equations in quantile regression for longitudinal data

  title={Smoothing combined estimating equations in quantile regression for longitudinal data},
  author={Chenlei Leng and Weiping Zhang},
  journal={Statistics and Computing},
Quantile regression has become a powerful complement to the usual mean regression. A simple approach to use quantile regression in marginal analysis of longitudinal data is to assume working independence. However, this may incur potential efficiency loss. On the other hand, correctly specifying a working correlation in quantile regression can be difficult. We propose a new quantile regression model by combining multiple sets of unbiased estimating equations. This approach can account for… CONTINUE READING

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Empirical likelihood and quantile regression in longitudinal data analysis

  • C. Y. Tang, C. Leng
  • Biometrika 98,
  • 2011
Highly Influential
3 Excerpts

Standard errors and covariance matrices for smoothed rank estimators

  • B. M. Brown, Y. G. Wang
  • Biometrika 92,
  • 2005
Highly Influential
12 Excerpts

Improving generalised estimating equations using quadratic inference functions

  • A. Qu, B. Lindsay, B. Li
  • Biometrika 87,
  • 2000
Highly Influential
7 Excerpts

Quasi-likelihood for median regression models

  • S. Jung
  • J. Am. Stat. Assoc
  • 1996
Highly Influential
9 Excerpts

On the distribution of a studentized quantile

  • P. Hall, S. J. Sheather
  • J. R. Stat. Soc. B
  • 1988
Highly Influential
6 Excerpts

Large sample properties of generalized method of moments estimators

  • P. L. Hansen
  • Econometrica 50,
  • 1982
Highly Influential
7 Excerpts

Hierarchical spline models for conditional quantiles and the demand for electricity

  • W. Hendricks, R. Koenker
  • J. Am. Stat. Assoc
  • 1992
Highly Influential
3 Excerpts

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