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Non-parametric estimation of spatial variation in relative risk.

Research output: Contribution to journalJournal articlepeer-review

<mark>Journal publication date</mark>15/11/1995
<mark>Journal</mark>Statistics in Medicine
Issue number21-22
Number of pages8
Pages (from-to)2335-2342
Publication StatusPublished
<mark>Original language</mark>English


We consider the problem of estimating the spatial variation in relative risks of two diseases, say, over a geographical region. Using an underlying Poisson point process model, we approach the problem as one of density ratio estimation implemented with a non-parametric kernel smoothing method. In order to assess the significance of any local peaks or troughs in the estimated risk surface, we introduce pointwise tolerance contours which can enhance a greyscale image plot of the estimate. We also propose a Monte Carlo test of the null hypothesis of constant risk over the whole region, to avoid possible over-interpretation of the estimated risk surface. We illustrate the capabilities of the methodology with two epidemiological examples.