title: Spatial regression with covariate measurement error: A semiparametric approach.
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-corrected estimates of regression parameters and derive their large sample properties. We evaluate the performance of the proposed method through simulation studies and illustrate using data on Ischemic Heart Disease (IHD). Both simulation and practical application demonstrate that the proposed method can be effective in practice.
authors.affiliation: School of Mathematical and Physical Sciences, University of Technology Sydney, Australia, 15 Broadway, Ultimo, NSW, 2007, Australia. MdHamidul.Huque@student.uts.edu.au.
authors.forename: Md Hamidul
authors.initials: MH
authors.lastname: Huque
authors.affiliation: Department of Statistics, North Carolina State University, 2311 Stinson Drive, Campus Box 8203, Raleigh, NC 27695-8203, USA.
authors.forename: Howard D
authors.initials: HD
authors.lastname: Bondell
authors.affiliation: Department of Statistics, 447 Blocker Building, Texas A&M University College Station, TX 77843-3143, USA.
authors.forename: Raymond J
authors.initials: RJ
authors.lastname: Carroll
authors.affiliation: School of Mathematical and Physical Sciences, University of Technology Sydney, Australia, 15 Broadway, Ultimo, NSW, 2007, Australia.
authors.forename: Louise M
authors.initials: LM
authors.lastname: Ryan
mesh_terms: D015982:Bias; D003198:Computer Simulation; D062306:Geography, Medical; D006801:Humans; D015233:Models, Statistical; D017202:Myocardial Ischemia / Q000453:epidemiology; D018401:Sample Size; D012959:Socioeconomic Factors; D000068916:Spatial Regression
keywords: Bivariate smoothing; Geoadditive models; Penalized least squares; Regression calibration; Socio-economic indexes for areas; Spatial linear model