# Penalized Likelihood Inference with Survey Data

arXiv: https://arxiv.org/abs/2304.07855 · HTML: https://mlsanity.com/paper/2304.07855/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 16 Apr 2023.

## Builds on (most cited first, 6 of 6)

- [A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm](https://mlsanity.com/paper/1902.03736/) (2019)
- [Exact post-selection inference, with application to the lasso](https://mlsanity.com/paper/1311.6238/) (2013)
- [Confidence Intervals and Hypothesis Testing for High-Dimensional Regression](https://mlsanity.com/paper/1306.3171/) (2013)
- [Post-Selection Inference for Generalized Linear Models with Many Controls](https://mlsanity.com/paper/1304.3969/) (2013)
- [Unifying approach to selective inference with applications to cross-validation](https://mlsanity.com/paper/1703.06559/) (2017)
- [De-biased Lasso for Generalized Linear Models with A Diverging Number of Covariates](https://mlsanity.com/paper/2110.04433/) (2021)

## 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.