Abstract
Poisson (3P) sampling is a commonly used method for generating estimates of timber volume. The usual estimator employed is the adjusted estimator, Y hata. The efficiency of this estimator can be greatly influenced by the presence of outliers. We formalize such a realistic situation for high-value timber estimation for which Y hata is inefficient. Here, yi approx beta xi for all but a few units in a population for which yi is large and xi very small. This situation can occur when estimating the net volume of high-value standing timber, such as that found in the Pacific Northwest region of the United States. A generalized regression estimator and an approximate Srivastava estimator are not affected by such data points. Simulations on a small population illustrate these ideas.
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