Application of James-Stein and empirical Bayes procedures to simultaneous estimation problems in forest inventory

Abstract

Traditional estimation procedures may ignore available auxilary information or use it only for regression, stratification, or in survey design. Such information, however, can be incorporated directly into estimation procedures. One case that has received much recent attention is where there exist K > 4 groups of simultaneous interest which are homogeneous (similar) with respect to their means. This paper describes two approaches (estimators) which incorporate auxilary information and thereby improve estimation efficiency as compared to commonly applied normal theory maximum likelihood estimators. Computer programs for implementation of the estimators are also described. Forest inventory problems provide an ideal application for such estimators. Simulation tests were conducted on four real forest populations covering volume and area estimation. Analysis of results showed consistent reductions in estimator total mean squared error. Confidence interval analyses are also presented.