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.

翻译：本文将三种Lasso推断方法——去偏Lasso、$C(\alpha)$检验和选择性推断——扩展至调查环境。我们在广义线性模型中，纳入调查权重和/或异方差性，建立了这些推断过程的渐近有效性。此外，我们将这些方法推广至非线性参数函数的推断，例如调查逻辑模型中的平均边际效应。我们通过模拟数据和2020年加拿大互联网使用调查数据展示了该方法的有效性。