Nonparametric modeling auxiliary covariates in random coefficient models

Published

Journal Article

Random coefficient model (RCM) is a powerful statistical tool in analyzing correlated data collected from studies with different clusters or from longitudinal studies. In practice, there is a need for statistical methods that allow biomedical researchers to adjust for the measured and unmeasured covariates that might affect the regression model. This article studies two nonparametric methods dealing with auxiliary covariate data in linear random coefficient models. We demonstrate how to estimate the coefficients of the models and how to predict the random effects when the covariates are missing or mismeasured. We employ empirical estimator and kernel smoother to handle a discrete and continuous auxiliary, respectively. Simulation results show that the proposed methods perform better than an alternative method that only uses data in the validation data set and ignores the random effects in the random coefficient model. Copyright © Taylor & Francis Group, LLC.

Full Text

Duke Authors

Cited Authors

  • Chen, J; Wang, X; Yang, D; Fan, B

Published Date

  • July 2, 2012

Published In

Volume / Issue

  • 41 / 8

Start / End Page

  • 1271 - 1281

Electronic International Standard Serial Number (EISSN)

  • 1532-4141

International Standard Serial Number (ISSN)

  • 0361-0918

Digital Object Identifier (DOI)

  • 10.1080/03610918.2011.594535

Citation Source

  • Scopus