Premium
Kernel estimation of risk surfaces without the need for edge correction
Author(s) -
Hazelton Martin L.
Publication year - 2007
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3047
Subject(s) - smoothing , bivariate analysis , kernel density estimation , kernel (algebra) , enhanced data rates for gsm evolution , statistics , kernel smoother , estimation , multivariate statistics , mathematics , computer science , variable kernel density estimation , kernel method , econometrics , artificial intelligence , economics , combinatorics , radial basis function kernel , management , estimator , support vector machine
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.