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Adjusted profile estimating function

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  • Molin Wang

Abstract

In settings where the full probability model is not specified, consider a general estimating function g(&thgr;, &lgr;; y) that involves not only the parameters of interest, &thgr;, but also some nuisance parameters, &lgr;. We consider methods for reducing the effects on g of fitting nuisance parameters. We propose Cox--Reid-type adjustment to the profile estimating function, g(&thgr;, &lgr;ˆ-sub-&thgr;; y), that reduces its bias by two orders. Typically, only the first two moments of the response variable are needed to form the adjustment. Important applications of this method include the estimation of the pairwise association and main effects in stratified, clustered data and estimation of the main effects in a matched pair study. A brief simulation study shows that the proposed method considerably reduces the impact of the nuisance parameters. Copyright Biometrika Trust 2003, Oxford University Press.

Suggested Citation

  • Molin Wang, 2003. "Adjusted profile estimating function," Biometrika, Biometrika Trust, vol. 90(4), pages 845-858, December.
    • Handle: RePEc:oup:biomet:v:90:y:2003:i:4:p:845-858
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    Cited by:

    1. Stefano Cabras & María Castellanos & Erlis Ruli, 2014. "A Quasi likelihood approximation of posterior distributions for likelihood-intractable complex models," METRON, Springer;Sapienza Università di Roma, vol. 72(2), pages 153-167, August.
    2. 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.
    3. Jun Yan & Jian Huang, 2009. "Partly Functional Temporal Process Regression with Semiparametric Profile Estimating Functions," Biometrics, The International Biometric Society, vol. 65(2), pages 431-440, June.

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