Using auxiliary information to estimate stand tables

The problem of estimating stand tables in stands with few sample points is considered. The usual point-sampling estimate of trees per hectare by diameter class is examined, along with two alternative estimators: a precision-weighted composite estimator and a pseudo-Bayes estimator. A large-scale forest inventory is simulated, and stand tables are estimated for each stand with each of the three estimators. Both the composite and pseudo-Bayes estimator appear superior (in terms of mean absolute error and mean squared error) to the usual estimator. The pseudo-Bayes estimator appears to perform the best (with an 80% reduction in mean squared error). This estimator also is easier to use than the composite estimator because it does not require within diameter class variance estimates.