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Articles containing the keyword 'bootstrap'
Category : Research article
article id 925,
category
Research article
(2013).
An assessment of three variance estimators for the k-nearest neighbour technique.
Silva Fennica
vol.
47
no.
1
article id 925.
https://doi.org/10.14214/sf.925
Keywords:
forest inventory;
simple random sampling;
resampling estimators;
bootstrap;
jackknife;
difference estimator;
cluster sampling
Abstract |
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Author Info
A jackknife (JK), a bootstrap (BOOT), and an empirical difference estimator (EDE) of totals and variance were assessed in simulated sampling from three artificial but realistic complex multivariate populations (N = 8000 elements) organized in clusters of four elements. Intra-cluster correlations of the target variables (Y) varied from 0.03 to 0.26. Time-saving implementations of JK and BOOT are detailed. In simple random sampling (SRS), bias in totals was ≤ 0.4% for the two largest sample sizes (n = 200, 300), but slightly larger for n = 50, and 100. In cluster sampling (CLU) bias was typically 0.1% higher and more variable. The lowest overall bias was in EDE. In both SRS and CLU, JK estimates of standard error were slightly (3%) too high, while the bootstrap estimates in both SRS and CLU were too low (8%). Estimates of error suggested a trend in EDE toward an overestimation with increasing sample size. Calculated 95% confidence intervals achieved a coverage that in most cases was fairly close (± 2%) to the nominal level. For estimation of a population total the EDE estimator appears to be slightly better than the JK estimator.
-
Magnussen,
Canadian Forest Service, Natural Resources Canada, 505 West Burnside Road, Victoria BC V8Z 1M5 Canada
E-mail:
steen.magnussen@nrcan.gc.ca
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