A Stochastic Restrictions Regression Model Approach to Volume Equation Estimation
Abstract:The stochastic restrictions regression estimator is suggested as a means of combining auxiliary information with standard regression applied to a data set. A derivation of the estimator is given, and the estimator is compared to other estimators that use auxiliary information and other regression approaches that have previously been applied in forestry. A test statistic for determining whether the auxiliary information is unbiased is presented along with a table of critical values. Approximate confidence interval procedures are obtained, and consistent means of estimating nuisance parameters are given. Finally, an example is provided to illustrate calculations. For. Sci. 36(1):54-65.
Document Type: Journal Article
Affiliations: Associate Professor, Department of Forest Resources, College of Natural Resources, University of Minnesota, St. Paul
Publication date: 1990-03-01
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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.
2015 Impact Factor: 1.702
Ranking: 16 of 66 in forestry
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