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Adjusted Inclusion Probabilities with 3P Sampling

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The mean square error efficiency of Grosenbaugh's (1964) adjusted 3P estimator is examined relative to four alternative estimators which use regression estimates of the conditional inclusion probabilities and true conditional inclusion probabilities. Actual mean square errors were calculated as the result of exhaustive sampling of populations that ranged in size from 9 elements to 144 elements and that exhibited widely different proportionality between the variable of interest and the auxiliary variable used for probability selection. The adjusted 3P estimator is as efficient or more efficient than all four competitors except when the unconditional probability of inclusion neared unity for some population elements. For. Sci. 33(3):617-631.
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Keywords: Adjusted 3P estimator; Poisson sampling; bias; mean square error

Document Type: Journal Article

Affiliations: Adjunct Professor, School of Forest Resources and Conservation, University of Florida, Gainesville

Publication date: 1987-09-01

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  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
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    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
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