Data Estimation and Prediction for Natural Resources Public Data PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Data Estimation and Prediction for Natural Resources Public Data PDF full book. Access full book title Data Estimation and Prediction for Natural Resources Public Data by Hans T. Schreuder. Download full books in PDF and EPUB format.
Author: Hans T. Schreuder Publisher: ISBN: Category : Forest surveys Languages : en Pages : 6
Book Description
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.
Author: Hans T. Schreuder Publisher: ISBN: Category : Forest surveys Languages : en Pages : 6
Book Description
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.
Author: American Statistical Association. Section on Statistics and the Environment Publisher: ISBN: Category : Environmental policy Languages : en Pages : 772
Author: Hans T. Schreuder
Author: Hans T. Schreuder
Author: 
Author: 
Author: Hans T. Schreuder
Author: Mark H. Hansen
Author: 
Author: 
Author: 
Author: 
Author: American Statistical Association. Section on Statistics and the Environment