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Using Monte Carlo Simulation to Evaluate Kernel-Based Home Range Estimators
Bruce J. Worton
The Journal of Wildlife Management
Vol. 59, No. 4 (Oct., 1995), pp. 794-800
Stable URL: http://www.jstor.org/stable/3801959
Page Count: 7
You can always find the topics here!Topics: Statistical estimation, Density estimation, Estimation methods, Data smoothing, Estimators, Estimators for the mean, Data ranges, Harmonic mean, Datasets, Data types
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Kernel density estimators have been used to estimate home range size but little is known of their statistical properties. I applied kernel-based estimators of home range size, calculated from 95% probability contours of nonparametric density estimators, to computer-simulated radiolocation data. Four hypothetical models of home range suggested by Boulanger and White (1990) were used to evaluate bias and precision of these estimators in estimating known home range sizes. Kernel methods compared well with the best methods that are available for home range size estimation provided the appropriate level of smoothing was selected. I used brush rabbit (Sylvilagus bachmani) telemetry data to illustrate how Monte Carlo methods may also be used to assess estimator performance from field radiolocation data. A kernel estimator is preferred to a harmonic mean estimator in this example because it is less biased (i.e., the harmonic mean method has an inherent problem).
The Journal of Wildlife Management © 1995 Wiley