Correlation and Marginal Longitudinal Kernel Nonparametric Regression

  • Oliver B. Linton
  • Enno Mammen
  • Xihong Lin
  • Raymond J. Carroll
Part of the Lecture Notes in Statistics book series (LNS, volume 179)

Abstract

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.

Keywords

Covariance Pepe 

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Copyright information

© Springer Science+Business Media New York 2004

Authors and Affiliations

  • Oliver B. Linton
    • 1
    • 2
    • 3
    • 4
  • Enno Mammen
    • 1
    • 2
    • 3
    • 4
  • Xihong Lin
    • 1
    • 2
    • 3
    • 4
  • Raymond J. Carroll
    • 1
    • 2
    • 3
    • 4
  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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