Skip to Main Content
Poisson sampling - The adjusted and unadjusted estimator revisitedAuthor(s): Michael S. Williams; Hans T. Schreuder; Gerardo H. Terrazas
Source: Res. Note. RMRS-RN-4. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 10 p.
Publication Series: Research Note (RN)
Station: Rocky Mountain Research Station
Download Publication (345 B)
DescriptionThe prevailing assumption, that for Poisson sampling the adjusted estimator "Y-hat a" is always substantially more efficient than the unadjusted estimator "Y-hat u" , is shown to be incorrect. Some well known theoretical results are applicable since "Y-hat a" is a ratio-of-means estimator and "Y-hat u" a simple unbiased estimator. We formalize an additional realistic situation for high-value timber estimation for which "Y-hat u" is more efficient. (Please note: equations are spelled out inside quotation marks. Please see PDF for symbols.)
- 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.
CitationWilliams, Michael S.; Schreuder, Hans T.; Terrazas, Gerardo H. 1998. Poisson sampling - The adjusted and unadjusted estimator revisited. Res. Note. RMRS-RN-4. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 10 p.
KeywordsPoisson sampling, adjusted estimator, unadjusted estimator, generalized regression estimator, approximate Srivastava estimator
- Use of change-point detection for friction-velocity threshold evaluation in eddy-covariance studies
- Model-based mean square error estimators for k-nearest neighbour predictions and applications using remotely sensed data for forest inventories
- Photo stratification improves northwest timber volume estimates.
XML: View XML