Almost universally, forest inventory and monitoring databases are incomplete, ranging from missing data for only a few records and a few variables, common for small land areas, to missing data for many observations and many variables, common for large land areas. For a wide variety of applications, nearest neighbor (NN) imputation methods have been developed to fill in observations of variables that are missing on some records (Y-variables), using related variables that are available for all records (X-variables). This review attempts to summarize the advantages and weaknesses of NN imputation methods and to give an overview of the NN approaches that have most commonly been used. It also discusses some of the challenges of NN imputation methods. The inclusion of NN imputation methods into standard software packages and the use of consistent notation may improve further development of NN imputation methods. Using X-variables from different data sources provides promising results, but raises the issue of spatial and temporal registration errors. Quantitative measures of the contribution of individual X-variables to the accuracy of imputing the Y-variables are needed. In addition, further research is warranted to verify statistical properties, modify methods to improve statistical properties, and provide variance estimators.
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input data for forest planning;
nearest neighbor imputation;
sources of X-variables
Document Type: Research Article
Department of Forest Engineering, Resources and Management, Oregon State University, Corvallis, Oregon, USA
Department of Forest Resources, University of British Columbia, Vancouver, Canada
Pacific Northwest Research Station, USDA Forest Service, Anchorage, Alaska, USA
Rocky Mountain Research Station, USDA Forest Service, Moscow, Idaho, USA
Publication date: 2009-06-01
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