Correlation and Marginal Longitudinal Kernel Nonparametric Regression

Part of the Lecture Notes in Statistics book series (LNS, volume 179)


We consider nonparametric regression in a marginal longitudinal data framework. Previous work ([3]) has shown that the kernel nonparametric regression methods extant in the literature for such correlated data have the discouraging property that they generally do not improve upon methods that ignore the correlation structure entirely. The latter methods are called working independence methods. We construct a two- stage kernel-based estimator that asymptotically uniformly improves upon the working independence estimator. A small simulation study is given in support of the asymptotics.


Nonparametric Regression Local Linear Regression Vary Coefficient Model Bias Expression Small Simulation Study 
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Copyright information

© Springer Science+Business Media New York 2004

Authors and Affiliations

  1. 1.London School of EconomicsUK
  2. 2.Heidelberg UniversityGermany
  3. 3.University of MichiganUSA
  4. 4.Texas A&M UniversityUSA

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