Evaluation of threshold selection methods for adaptive kernel density estimation in disease mapping

Evaluation of threshold selection methods for adaptive kernel density estimation in disease mapping

Abstract Background Maps of disease rates produced without careful consideration of the underlying population distribution may be unreliable due to the well-known small numbers problem. Smoothing methods such as Kernel Density Estimation (KDE) are employed to control the population basis of spatial...

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Bibliographic Details
Main Authors: Warangkana Ruckthongsook, Chetan Tiwari, Joseph R. Oppong, Prathiba Natesan
Format: Article
Language:English
Published: BMC 2018-05-01
Series:International Journal of Health Geographics
Subjects:
Kernel density estimation
Bandwidth selection
Threshold
Monte Carlo simulation
Disease mapping
Online Access:http://link.springer.com/article/10.1186/s12942-018-0129-9

Internet

http://link.springer.com/article/10.1186/s12942-018-0129-9

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