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Penalized Likelihood Inference with Survey Data

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  • Joann Jasiak
  • Purevdorj Tuvaandorj

Abstract

This paper extends three Lasso inferential methods, Debiased Lasso, $C(\alpha)$ and Selective Inference to a survey environment. We establish the asymptotic validity of the inference procedures in generalized linear models with survey weights and/or heteroskedasticity. Moreover, we generalize the methods to inference on nonlinear parameter functions e.g. the average marginal effect in survey logit models. We illustrate the effectiveness of the approach in simulated data and Canadian Internet Use Survey 2020 data.

Suggested Citation

  • Joann Jasiak & Purevdorj Tuvaandorj, 2023. "Penalized Likelihood Inference with Survey Data," Papers 2304.07855, arXiv.org.
    • Handle: RePEc:arx:papers:2304.07855
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    File URL: http://arxiv.org/pdf/2304.07855
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    References listed on IDEAS

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    1. Wooldridge, Jeffrey M., 2001. "Asymptotic Properties Of Weighted M-Estimators For Standard Stratified Samples," Econometric Theory, Cambridge University Press, vol. 17(2), pages 451-470, April.
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    4. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2016. "Post-Selection Inference for Generalized Linear Models With Many Controls," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 606-619, October.
    5. Xiaoying Tian & Jonathan Taylor, 2017. "Asymptotics of Selective Inference," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(2), pages 480-499, June.
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    Cited by:

    1. Joann Jasiak & Peter MacKenzie & Purevdorj Tuvaandorj, 2023. "Digital Divide: Empirical Study of CIUS 2020," Papers 2301.07855, arXiv.org, revised Dec 2023.

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