Data estimation and prediction for natural resources public data / Hans T. Schreuder, Robin M. Reich.

Format:

Book

Government document

Author/Creator:

Schreuder, Hans T., author.

Reich, Robin M., author.

Contributor:

Rocky Mountain Research Station (Fort Collins, Colo.), issuing body.

Series:

Research note RMRS ; 2.

Research note RMRS ; 2

Language:

English

Subjects (All):

Forest surveys--United States--Databases--Management.

Forest surveys.

Natural resources surveys--United States--Databases--Management.

Natural resources surveys.

Multiple imputation (Statistics).

Missing observations (Statistics).

United States.

Physical Description:

1 online resource (5 pages).

Place of Publication:

Fort Collins, CO : United States Department of Agriculture, Forest Service, Rocky Mountain Research Station, 1998.

Summary:

A key product of both Forest Inventory and Analysis (FIA) of the USDA Forest Service and the Natural Resources Inventory (NRI) of the Natural Resources Conservation Service is a scientific data base that should be defensible in court. Multiple imputation procedures (MIPs) have been proposed both for missing value estimation and prediction of non-remeasured cells in annualized forest inventories such as the Southern Annual Forest Inventory System (SAFIS). MIPs generate clean-looking data bases that are easily used but hide a serious weakness: under different assumptions made by reasonable people, very different data bases and conclusions can be generated. A MIP is an interesting idea for prediction but should only be used for analyses by users, not for filling in data in a public data base. Simple illustrations are given to make our points. To maintain a defensible data base, FIA and NRI should only provide algorithms to facilitate user-generated data for prediction of non-remeasured cells. Users, not FIA and NRI, should be responsible for generating data bases that utilize these algorithms or other algorithms of their choosing, incorporating assumptions that they are willing to make. But they should be encouraged to work with FIA and NRI personnel in utilizing such algorithms.

Notes:

Includes bibliographical references (pages 4-5).

Electronic reproduction. [S.l.] : HathiTrust Digital Library, 2010.

Other Format:

Print version: Schreuder, Hans T. Data estimation and prediction for natural resources public data

Microfiche version: Schreuder, Hans T. Data estimation and prediction for natural resources public data

OCLC:

681127696

Access Restriction:

Use copy Restrictions unspecified