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Robust inference for sparse cluster-correlated count data

Author

Listed:
  • Hanfelt, John J.
  • Li, Ruosha
  • Pan, Yi
  • Payment, Pierre

Abstract

Standard methods for the analysis of cluster-correlated count data fail to yield valid inferences when the study is finely stratified and the interest is in assessing the intracluster correlation structure. We present an approach, based upon exactly adjusting an estimating function for the bias induced by the fitting of stratum-specific effects, that requires modeling only the first two joint moments of the observations and that yields consistent and asymptotically normal estimators of the correlation parameters.

Suggested Citation

  • Hanfelt, John J. & Li, Ruosha & Pan, Yi & Payment, Pierre, 2011. "Robust inference for sparse cluster-correlated count data," Journal of Multivariate Analysis, Elsevier, vol. 102(1), pages 182-192, January.
    • Handle: RePEc:eee:jmvana:v:102:y:2011:i:1:p:182-192
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    References listed on IDEAS

    as
    1. John J. Hanfelt, 2003. "Conditioning to reduce the sensitivity of general estimating functions to nuisance parameters," Biometrika, Biometrika Trust, vol. 90(3), pages 517-531, September.
    2. Thomas A. Severini, 2002. "Modified estimating functions," Biometrika, Biometrika Trust, vol. 89(2), pages 333-343, June.
    3. Molin Wang, 2003. "Adjusted profile estimating function," Biometrika, Biometrika Trust, vol. 90(4), pages 845-858, December.
    4. Molin Wang & John J. Hanfelt, 2007. "Orthogonal locally ancillary estimating functions for matched pair studies and errors in covariates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(3), pages 411-428, June.
    5. Huang Y. & Wang C.Y., 2001. "Consistent Functional Methods for Logistic Regression With Errors in Covariates," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1469-1482, December.
    6. John J. Hanfelt, 2004. "Composite conditional likelihood for sparse clustered data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(1), pages 259-273, February.
    Full references (including those not matched with items on IDEAS)

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