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Moment estimation in a semiparametric generalized linear model

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  • Wang, Xueqin
  • Peng, Hanxiang

Abstract

In this article, we propose to estimate the regression parameters in a semiparametric generalized linear model by moment estimating equations. These estimators are shown to be consistent and asymptotically normal. We present two estimators of the nonparametric part, provide conditions for the existence and uniform consistency, and obtain faster rates of convergence under weaker assumptions.

Suggested Citation

  • Wang, Xueqin & Peng, Hanxiang, 2008. "Moment estimation in a semiparametric generalized linear model," Statistics & Probability Letters, Elsevier, vol. 78(12), pages 1624-1633, September.
    • Handle: RePEc:eee:stapro:v:78:y:2008:i:12:p:1624-1633
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    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(08)00021-7
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    References listed on IDEAS

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    1. Forrester Jeffrey S. & Hooper William J. & Peng Hanxiang & Schick Anton, 2003. "On the construction of efficient estimators in semiparametric models," Statistics & Risk Modeling, De Gruyter, vol. 21(2/2003), pages 109-138, February.
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    Cited by:

    1. Hanxiang Peng, 2008. "Efficient inference in a semiparametric generalised linear model," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 20(2), pages 115-127.

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