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In environmental health research, it is of interest to understand the effectof the neighborhood environment on health. Researchers have shown a protectiveassociation between green space around a person's residential address anddepression outcomes. In measuring exposure to green space, distance buffers areoften used. However, buffer distances differ across studies. Typically, thebuffer distance is determined by researchers a priori. It is unclear how toidentify an appropriate buffer distance for exposure assessment. To addressgeographic uncertainty problem for exposure assessment, we present a domainselection algorithm based on the penalized functional linear Cox regressionmodel. The theoretical properties of our proposed method are studied andsimulation studies are conducted to evaluate finite sample performances of ourmethod. The proposed method is illustrated in a study of associations of greenspace exposure with depression and/or antidepressant use in the Nurses' HealthStudy.

A Penalized Functional Linear Cox Regression Model for Spatially-defined Environmental Exposure with an Estimated Buffer Distance