Linear Regression Model with a Randomly Censored Predictor: Estimation Procedures

Folefac D Atem and Roland A Matsouaka
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Folefac D Atem: Department of Biostatistics, University of Texas Health Science Center, USA
Roland A Matsouaka: Department of Biostatistics and Bioinformatics & Duke Clinical Research Institute, Duke University, USA

Biostatistics and Biometrics Open Access Journal, 2017, vol. 1, issue 2, 21-32

Abstract: We consider linear regression model estimation where the covariate of interest is randomly censored. Under a non-informative censoring mechanism, one may obtain valid estimates by deleting censored observations. However, this comes at a cost of lost in formation and decreased efficiency, especially under head censoring. Other methods for dealing with censored covariates such a signoring censoring or replacing censored observations with a fixed number, often lead to severely biased results and are of limited practicality.

Keywords: Biometrics Open Access Journal; Biostatistics and Biometrics; Biostatistics and Biometrics Open Access Journal; Open Access Journals; biometrics journal; biometrics articles; biometrics journal reference; biometrics journal impact factor; biometrics and biostatistics journal impact factor; journal of biometrics; open access juniper publishers; juniper publishers reivew (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:adp:jbboaj:v:1:y:2017:i:2:p:21-32

DOI: 10.19080/BBOAJ.2017.01.555556

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