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Combining Inventory Estimates with Possibly Biased Auxiliary Information

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The usual method of combining sample data with auxiliary information is the familiar precision-weighted composite estimator. However, application of this estimator is straightforward only when the auxiliary information is unbiased. If this is not the case, and the bias is unaccounted for, then the risk of the usual composite estimator can be greater than that of the sample mean. We conjecture that investigators are often unsure of the possible bias in their auxiliary information. Accordingly, we develop an estimator which mimics the usual composite estimator when the auxiliary information is unbiased, yet dominates the sample mean even as the bias becomes large. In addition, unlike the usual composite estimator, the new estimator does not require one to specify the variance of the auxiliary information. The performance of both estimators, and the sample mean, is examined based on results of a simulation trial on actual sample data. For. Sci 36(3):693-704.
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Keywords: James-Stein estimation; composite estimators; empirical Bayes estimation

Document Type: Journal Article

Affiliations: Professor and Chairman, Department of Statistics, Rutgers University, New Brunswick, NJ, 08903

Publication date: 1990-09-01

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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.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2016 Impact Factor: 1.782 (Rank 17/64 in forestry)

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
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