Tutorial on kernel estimation of continuous spatial and spatiotemporal relative risk.
Kernel smoothing is a highly flexible and popular approach for estimation of probability density and intensity functions of continuous spatial data. In this role, it also forms an integral part of estimation of functionals such as the density-ratio or "relative risk" surface. Originally developed wi...
| Publicado en: | Statistics in Medicine Vol. 37; no. 7; pp. 1191 - 1222 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
3/30/2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=128331355&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 128331355 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02776715 2DZ jtl: Statistics in Medicine issn: 02776715 maglogo: Y pubinfo: dt: 3/30/2018 vid: 37 iid: 7 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 128331355 128331355 NLM29226352 128331355 10.1002/sim.7577 NLM29226352 128331355 ppf: 1191 ppct: 31 formats: tig: atl: Tutorial on kernel estimation of continuous spatial and spatiotemporal relative risk. aug: au: Davies, Tilman M. Marshall, Jonathan C. Hazelton, Martin L. affil: Department of Mathematics & Statistics, University of Otago, Dunedin, New Zealand sug: subj: Statistics Methods Risk Assessment Methods Data Analysis, Statistical Systems Analysis Human Software Reproducibility of Results Computer Simulation Relative Risk ab: Kernel smoothing is a highly flexible and popular approach for estimation of probability density and intensity functions of continuous spatial data. In this role, it also forms an integral part of estimation of functionals such as the density-ratio or "relative risk" surface. Originally developed with the epidemiological motivation of examining fluctuations in disease risk based on samples of cases and controls collected over a given geographical region, such functions have also been successfully used across a diverse range of disciplines where a relative comparison of spatial density functions has been of interest. This versatility has demanded ongoing developments and improvements to the relevant methodology, including use spatially adaptive smoothers; tests of significantly elevated risk based on asymptotic theory; extension to the spatiotemporal domain; and novel computational methods for their evaluation. In this tutorial paper, we review the current methodology, including the most recent developments in estimation, computation, and inference. All techniques are implemented in the new software package sparr, publicly available for the R language, and we illustrate its use with a pair of epidemiological examples. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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