Journal article
Spatial regression with covariate measurement error: A semiparametric approach
MH Huque, HD Bondell, RJ Carroll, LM Ryan
Biometrics | OXFORD UNIV PRESS | Published : 2016
DOI: 10.1111/biom.12474
Abstract
Spatial data have become increasingly common in epidemiology and public health research thanks to advances in GIS (Geographic Information Systems) technology. In health research, for example, it is common for epidemiologists to incorporate geographically indexed data into their studies. In practice, however, the spatially defined covariates are often measured with error. Naive estimators of regression coefficients are attenuated if measurement error is ignored. Moreover, the classical measurement error theory is inapplicable in the context of spatial modeling because of the presence of spatial correlation among the observations. We propose a semiparametric regression approach to obtain bias-..
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Awarded by National Cancer Institute
Funding Acknowledgements
The authors thank the coeditor and the associated editor for constructive comments which led to considerable improvement of the manuscript. HDB was partially supported as a visitor at the School of Mathematical and Physical Sciences, University of Technology Sydney, and by grants NSF DMS-1308400 and NIH P01-CA142538. RC was partially supported by the National Cancer Institute grant U01-CA057030. LR and HH were supported by the University of Technology Sydney and by the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). The authors thank the NSW Ministry of Health for making the data available.