# High-dimensional inference in misspecified linear models

arXiv: https://arxiv.org/abs/1503.06426 · HTML: https://mlsanity.com/paper/1503.06426/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2015.

## Built on by (most cited first, 2 of 2)

- [DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R](https://mlsanity.com/paper/2103.09603/) (2021)
- [Debiased Estimators in High-Dimensional Regression: A Review and Replication of Javanmard and Montanari (2014)](https://mlsanity.com/paper/2604.00848/) (2026)

## Abstract (arXiv)

We consider high-dimensional inference when the assumed linear model is misspecified. We describe some correct interpretations and corresponding sufficient assumptions for valid asymptotic inference of the model parameters, which still have a useful meaning when the model is misspecified. We largely focus on the de-sparsified Lasso procedure but we also indicate some implications for (multiple) sample splitting techniques. In view of available methods and software, our results contribute to robustness considerations with respect to model misspecification.