JSM 2005 - Toronto

Abstract #302566

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 301
Type: Invited
Date/Time: Tuesday, August 9, 2005 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract - #302566
Title: Use of Canonical Regression in Most Similar Neighbor Inference Evaluated by Partitioning Imputation Error
Author(s): Nicholas L. Crookston and Albert R. Stage*+
Companies: Rocky Mountain Research Station and Rocky Mountain Research Station
Address: Forestry Sciences Laboratory, Moscow, ID, 83843,
Keywords:
Abstract:

Imputation is applied for two purposes: to supply missing data to complete a database for subsequent modeling analyses and to support estimates of subpopulation totals. Error properties of the imputed values have different roles in these two contexts. In turn, the imputation errors depend on the sampling procedures of the inventory as well as on the analytical process selected for imputation. We define a partitioning of imputation error to inform the choice among imputation methods. Statistics based on this partitioning are used to compare two alternative formulations of the similarity measure in Most Similar Neighbor (MSN) imputation. MSN analysis uses a squared distance measure of similarity between an observation and its prospective surrogate that is a quadratic form in the predictor variables known for all sample units. The weight matrix in the quadratic form is derived from either multivariate canonical correlation analysis or its closely related canonical regression. We discuss how these measures of similarity are influenced by the components of error inherent in the variables and the functional relations between the predictor variables and the variable.

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