Penalized Likelihood Inference with Survey Data
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Abstract (arXiv)
This paper extends three Lasso inferential methods, Debiased Lasso, C(α) 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.
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References · 6
- 2019A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Normcited by 106
- 2013Exact post-selection inference, with application to the lassocited by 91
- 2013Confidence Intervals and Hypothesis Testing for High-Dimensional Regressioncited by 79
- 2013Post-Selection Inference for Generalized Linear Models with Many Controlscited by 11
- 2017Unifying approach to selective inference with applications to cross-validationcited by 9
- 2021De-biased Lasso for Generalized Linear Models with A Diverging Number of Covariatescited by 3
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