Are kernels the mustard? Data from global positioning system (GPS) collars suggests problems for kernel home‐range analyses with least‐squares cross‐validation

Hemson, Graham; A. Johnson, Paul; South, Andy; Kenward, Robert; M. Ripley, Ruth; W. Macdonald, David

doi:10.1111/j.1365-2656.2005.00944.x

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Are kernels the mustard? Data from global positioning system (GPS) collars suggests problems for kernel home‐range analyses with least‐squares cross‐validation

Shared by NobleBlocks on May 1, 2005 • 12:00 AM UTC

Authors:

Graham Hemson

Paul A. Johnson

Andy South

Abstract

Summary Kernel‐density estimation (KDE) is one of the most widely used home‐range estimators in ecology. The recommended implementation uses least squares cross‐validation (LSCV) to calculate the smoothing factor ( h ) which has a considerable influence on the home‐range estimate. We tested the perf...

Subject

Estimator

Statistics

Kernel density estimation

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