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A two-step approach for variable selection in linear regression with measurement error

Authors
Song, JiyeonShin, Seung Jun
Issue Date
1월-2019
Publisher
KOREAN STATISTICAL SOC
Keywords
measurement error; penalized orthogonal regression; SIMEX
Citation
COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS, v.26, no.1, pp.47 - 55
Indexed
SCOPUS
KCI
Journal Title
COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS
Volume
26
Number
1
Start Page
47
End Page
55
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/68443
DOI
10.29220/CSAM.2019.26.1.047
ISSN
2287-7843
Abstract
It is important to identify informative variables in high dimensional data analysis; however, it becomes a challenging task when covariates are contaminated by measurement error due to the bias induced by measurement error. In this article, we present a two-step approach for variable selection in the presence of measurement error. In the first step, we directly select important variables from the contaminated covariates as if there is no measurement error. We then apply, in the following step, orthogonal regression to obtain the unbiased estimates of regression coefficients identified in the previous step. In addition, we propose a modification of the twostep approach to further enhance the variable selection performance. Various simulation studies demonstrate the promising performance of the proposed method.
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