Kernel estimation of risk surfaces without the need for edge correction

Abstract

Kernel estimates of relative risk surfaces can be used to examine the geographical variation of disease risk. These surfaces can be expressed as ratios of bivariate kernel density estimates constructed from case and control data, but care must be taken to avoid excessive bias at the boundaries of the region under study. It is possible to correct this bias, without the complications of explicit edge correction, through the use of a specific smoothing regimen. Copyright © 2007 John Wiley & Sons, Ltd.