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A note on nonparametric estimation for clustered data

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  • Huggins, Richard

Abstract

The generalized estimating equation approach to nonparametric estimation using clustered data has a counter intuitive feature: the most asymptotically efficient estimator ignores the dependence within the clusters J. Amer. Statist. Assoc. 95 (2000) 520. Using an example with highly dependent data we present an alternate procedure that does account for dependence. The superior results of this procedure suggest a flaw in previous extensions of the generalized estimating equations to nonparametric inference. We show that, that efficient nonparametric estimation requires a different class of estimating equations wherein dependence is incorporated via an offset term.

Suggested Citation

  • Huggins, Richard, 2004. "A note on nonparametric estimation for clustered data," Statistics & Probability Letters, Elsevier, vol. 69(2), pages 129-133, August.
  • Handle: RePEc:eee:stapro:v:69:y:2004:i:2:p:129-133
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    References listed on IDEAS

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    1. Naisyin Wang, 2003. "Marginal nonparametric kernel regression accounting for within-subject correlation," Biometrika, Biometrika Trust, vol. 90(1), pages 43-52, March.
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