Title
STRATIFICATION AND CLUSTER ESTIMATOR ON AN AREA FRAME BY SQUARED SEGMENTS WITH AN ALIGNED SAMPLE
Abstract
Several European countries (Portugal, Spain, Greece, Rumania, and the Czech Republic) make crop surveys on area frame with an aligned systematic sampling of squared segments. So far crop area estimates are obtained with standard formulae for random sampling, without using the spatial structure of the sample. This is in general conservative, the estimated standard error is larger than the error actually made. Taking as clusters the set of segments with the same relative position in a block, gives often lower but very unstable variances. A more stable variance estimate is computed by repeated random permutations of the sample segments in each block before building up the clusters. There is most often a moderate variance reduction when no stratification has been performed. If the sample is stratified and the cluster estimator is applied in each stratum, the variances seem to be less unstable. In this case permutations in each block seem not to be very useful.
Keywords
Area frame sampling, cluster estimator, crop area estimation, stratification
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Fuentes, M. and Gallego, F J.
(1994).
"STRATIFICATION AND CLUSTER ESTIMATOR ON AN AREA FRAME BY SQUARED SEGMENTS WITH AN ALIGNED SAMPLE,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1353
STRATIFICATION AND CLUSTER ESTIMATOR ON AN AREA FRAME BY SQUARED SEGMENTS WITH AN ALIGNED SAMPLE
Several European countries (Portugal, Spain, Greece, Rumania, and the Czech Republic) make crop surveys on area frame with an aligned systematic sampling of squared segments. So far crop area estimates are obtained with standard formulae for random sampling, without using the spatial structure of the sample. This is in general conservative, the estimated standard error is larger than the error actually made. Taking as clusters the set of segments with the same relative position in a block, gives often lower but very unstable variances. A more stable variance estimate is computed by repeated random permutations of the sample segments in each block before building up the clusters. There is most often a moderate variance reduction when no stratification has been performed. If the sample is stratified and the cluster estimator is applied in each stratum, the variances seem to be less unstable. In this case permutations in each block seem not to be very useful.