Inference in High-Dimensional Linear Measurement Error Models
Methodology
2020-01-29 v1
Abstract
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.
Cite
@article{arxiv.2001.10142,
title = {Inference in High-Dimensional Linear Measurement Error Models},
author = {Mengyan Li and Runze Li and Yanyuan Ma},
journal= {arXiv preprint arXiv:2001.10142},
year = {2020}
}