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Partitioning Error Components for Accuracy-Assessment of Near-Neighbor Methods of Imputation

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Imputation is applied for two quite different purposes: to supply missing data to complete a data set for subsequent modeling analyses or to estimate subpopulation totals. Error properties of the imputed values have different effects in these two contexts. We partition errors of imputation derived from similar observation units as arising from three sources: observation error, the distribution of observation units with respect to their similarity, and pure error given a particular choice of variables known for all observation units. Two new statistics based on this partitioning measure the accuracy of the imputations, facilitating comparison of imputation to alternative methods of estimation such as regression and comparison of alternative methods of imputation generally. Knowing the relative magnitude of the errors arising from these partitions can also guide efficient investment in obtaining additional data. We illustrate this partitioning using three extensive data sets from western North America. Application of this partitioning to compare near-neighbor imputation is illustrated for Mahalanobis- and two canonical correlation-based measures of similarity.
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Keywords: k-nn inference; landscape modeling; missing data; most similar neighbor

Document Type: Research Article

Publication date: 2007-02-01

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