# Inference in High-Dimensional Linear Measurement Error Models

arXiv: https://arxiv.org/abs/2001.10142 · HTML: https://mlsanity.com/paper/2001.10142/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2020.

## Built on by (most cited first, 1 of 1)

- [On High dimensional Poisson models with measurement error: hypothesis testing for nonlinear nonconvex optimization](https://mlsanity.com/paper/2301.00139/) (2022)

## Abstract (arXiv)

For a high-dimensional linear model with a finite number of covariates measured with error, we study statistical inference on the parameters associated with the error-prone covariates, and propose a new corrected decorrelated score test and the corresponding one-step estimator. We further establish asymptotic properties of the newly proposed test statistic and the one-step estimator. Under local alternatives, we show that the limiting distribution of our corrected decorrelated score test statistic is non-central normal. The finite-sample performance of the proposed inference procedure is examined through simulation studies. We further illustrate the proposed procedure via an empirical analysis of a real data example.