Skip to Main Content
Complex sample survey estimation in static state-spaceAuthor(s): Raymond L. Czaplewski
Source: Gen. Tech. Rep. RMRS-GTR-239. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 124 p.
Publication Series: General Technical Report (GTR)
Station: Rocky Mountain Research Station
Download Publication (1.63 MB)
DescriptionIncreased use of remotely sensed data is a key strategy adopted by the Forest Inventory and Analysis Program. However, multiple sensor technologies require complex sampling units and sampling designs. The Recursive Restriction Estimator (RRE) accommodates this complexity. It is a design-consistent Empirical Best Linear Unbiased Prediction for the state-vector, which contains all sufficient statistics for the sampled population. RRE reduces a complex estimator into a sequence of simpler estimators. Also included are model-based pseudo-estimators and multivariate Taylor series approximations for covariance matrices. Together, these provide a unifi ed approach to detailed estimation in large, complex sample surveys.
- You may send email to firstname.lastname@example.org to request a hard copy of this publication.
- (Please specify exactly which publication you are requesting and your mailing address.)
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationCzaplewski, Raymond L. 2010. Complex sample survey estimation in static state-space. Gen. Tech. Rep. RMRS-GTR-239. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 124 p.
KeywordsFIA, sampling, recursive, Pythagorean regression, EBLUP, remote sensing
- Accuracy assessment with complex sampling designs
- Assessing accuracy of point fire intervals across landscapes with simulation modelling
- Numerically stable algorithm for combining census and sample estimates with the multivariate composite estimator
XML: View XML