A three-phase sampling extension of the generalized regression estimator with partially exhaustive information
- 1 April 2014
- journal article
- Published by Canadian Science Publishing in Canadian Journal of Forest Research
- Vol. 44 (4), 383-388
- https://doi.org/10.1139/cjfr-2013-0449
Abstract
We consider three-phase sampling schemes in which one component of the auxiliary information is known in the very large sample of the so-called null phase and the second component is available only in the large sample of the first phase, whereas the second phase provides the terrestrial inventory data. We extend to three-phase sampling the generalized regression estimator that applies when the null phase is exhaustive, for global and local estimation, and derive its asymptotic design-based variance. The new three-phase regression estimator is particularly useful for reducing substantially the computing time required to treat exhaustively very large data sets generated by modern remote sensing technology such as LiDAR.Keywords
This publication has 5 references indexed in Scilit:
- New regression estimators in forest inventories with two-phase sampling and partially exhaustive information: a design-based Monte Carlo approach with applications to small-area estimationCanadian Journal of Forest Research, 2013
- Design-based properties of some small-area estimators in forest inventory with two-phase samplingCanadian Journal of Forest Research, 2013
- A three-phase sampling procedure for continuous forest inventory with partial re-measurement and updating of terrestrial sample plotsEuropean Journal of Forest Research, 2012
- Sampling Techniques for Forest InventoriesPublished by Taylor & Francis Ltd ,2007
- A three-phase sampling strategy for large-scale multiresource forest inventoriesJournal of Agricultural, Biological and Environmental Statistics, 2006