Skip to main content

Statistical Properties of Alternative National Forest Inventory Area Estimators

Buy Article:

$21.50 plus tax (Refund Policy)

The statistical properties of potential estimators of forest area for the USDA Forest Service's Forest Inventory and Analysis (FIA) program are presented and discussed. The current FIA area estimator is compared and contrasted with a weighted mean estimator and an estimator based on the Polya posterior, in the presence of nonresponse. Estimator optimality is evaluated both theoretically and via simulation under bias and mean squared error criteria. The results indicate that, under realistic conditions, the current FIA area estimator can sometimes result in substantial bias and have a higher mean squared error than both of the alternative estimators. This finding is of special interest because the same factor that contributes to this increased bias and variance applies to all area-based FIA estimates. The weighted mean and Polya posterior estimators gave similar results for estimating the total area of a domain. It is concluded that the main advantage of the latter approach is that many other statistics are obtainable because the entire population distribution is estimated from the same sampling effort. The cost of this advantage for the Polya posterior approach is that a single result requires many more computer operations, a cost that has become virtually ignorable over the past decade.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: Polya posterior; nonresponse

Document Type: Research Article

Publication date: 2012-12-02

More about this publication?
  • Important Notice: SAF's journals are now published through partnership with the Oxford University Press. Access to archived material will be available here on the Ingenta website until March 31, 2018. For new material, please access the journals via OUP's website. Note that access via Ingenta will be permanently discontinued after March 31, 2018. Members requiring support to access SAF's journals via OUP's site should contact SAF's membership department for assistance.

    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:
    Journal of Forestry
    Other SAF Publications
  • Submit a Paper
  • Membership Information
  • Author Guidelines
  • Podcasts
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more