Inference in high dimensional linear measurement error models

Mengyan Li, Runze Li and Yanyuan Ma

Journal of Multivariate Analysis, 2021, vol. 184, issue C

Abstract: For a high dimensional linear model with a finite number of covariates measured with errors, we study statistical inference on the parameters associated with the error-prone covariates, and propose a new corrected decorrelated score test and a corresponding score type estimator. This work was motivated by a real data example, where both low dimensional phenotypic variables and high dimensional genotypic variables, single nucleotide polymorphisms (SNPs), are available. One of the phenotypic variables is of clinical interest but measured with error. As is standard in the literature, the high dimensional SNPs are assumed to be measured accurately.

Keywords: Decorrelation; High dimensional inference; High dimensional nuisance parameters; Measurement error model (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.jmva.2021.104759

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