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On doubly robust estimation for logistic partially linear models

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  • Tan, Zhiqiang

Abstract

Consider a logistic partially linear model, in which the logit of the mean of a binary response is related to a linear function of some covariates and a nonparametric function of other covariates. We derive simple, doubly robust estimators of coefficients in the linear component. Such estimators remain consistent if either a nuisance model is correctly specified for the nonparametric component, or another nuisance model is correctly specified for the means of the covariates of interest given other covariates and the response at a fixed value.

Suggested Citation

  • Tan, Zhiqiang, 2019. "On doubly robust estimation for logistic partially linear models," Statistics & Probability Letters, Elsevier, vol. 155(C), pages 1-1.
    • Handle: RePEc:eee:stapro:v:155:y:2019:i:c:17
    DOI: 10.1016/j.spl.2019.108577
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    References listed on IDEAS

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    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. Eric J. Tchetgen Tchetgen & James M. Robins & Andrea Rotnitzky, 2010. "On doubly robust estimation in a semiparametric odds ratio model," Biometrika, Biometrika Trust, vol. 97(1), pages 171-180.
    3. Hua Yun Chen, 2007. "A Semiparametric Odds Ratio Model for Measuring Association," Biometrics, The International Biometric Society, vol. 63(2), pages 413-421, June.
    4. Farrell, Max H., 2015. "Robust inference on average treatment effects with possibly more covariates than observations," Journal of Econometrics, Elsevier, vol. 189(1), pages 1-23.
    5. Chamberlain, Gary, 1987. "Asymptotic efficiency in estimation with conditional moment restrictions," Journal of Econometrics, Elsevier, vol. 34(3), pages 305-334, March.
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    Cited by:

    1. Stijn Vansteelandt & Oliver Dukes, 2022. "Assumption‐lean inference for generalised linear model parameters," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 657-685, July.

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