In search of a variance estimator for systematic sampling |
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| Authors: | Steen Magnussen Lutz Fehrmann |
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| Institution: | 1. Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre, Victoria, Canada;2. Forest Inventory and Remote Sensing, Faculty of Forest Sciences, University of G?ttingen, G?ttingen, Germany |
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| Abstract: | Seven variance estimators to be used under systematic sampling are evaluated in a simulation study with 270 artificial spatial populations with different levels and structure of autocorrelation. In settings without an auxiliary variable a proposed new spatial resampling estimator RHO is recommended. In setting with an auxiliary variable, an estimator based on post-stratification (PST), and one with a correction for spatial autocorrelation (DOR), generated estimates with less bias than the SRS estimator in the majority of studied settings. Only in populations with either a near zero autocorrelation at the interval of sampling, or a very strong correlation between the target and the auxiliary variable did the otherwise conservative SRS estimator perform as well as the alternatives. |
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| Keywords: | Simple random sampling random tessellated stratified estimators spatial autocorrelation first-order autoregression Voronoi tessellation |
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