Journal
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume 54, Issue 12, Pages 3095-3107Publisher
ELSEVIER
DOI: 10.1016/j.csda.2009.11.023
Keywords
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Funding
- Council of the Czech Government [MSM 6198959214]
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New imputation algorithms for estimating missing values in compositional data are introduced. A first proposal uses the k-nearest neighbor procedure based on the Aitchison distance, a distance measure especially designed for compositional data. It is important to adjust the estimated missing values to the overall size of the compositional parts of the neighbors. As a second proposal an iterative model-based imputation technique is introduced which initially starts from the result of the proposed k-nearest neighbor procedure. The method is based on iterative regressions, thereby accounting for the whole multivariate data information. The regressions have to be performed in a transformed space, and depending on the data quality classical or robust regression techniques can be employed. The proposed methods are tested on a real and on simulated data sets. The results show that the proposed methods outperform standard imputation methods. In the presence of outliers, the model-based method with robust regressions is preferable. (C) 2009 Elsevier B.V. All rights reserved.
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