Louis/Thèse/Lectures/@hronImputationMissingValues2010.md

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literature note	Note de lecture de *Imputation of missing values for compositional data using classical and robust methods* de K. Hron, M. Templ, P. Filzmoser.	../these_ref.bib

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FirstAuthor:: Hron, K.
Author:: Templ, M.
Author:: Filzmoser, P.

Title:: Imputation of missing values for compositional data using classical and robust methods

Year:: 2010

Citekey:: hronImputationMissingValues2010

itemType:: journalArticle

Journal:: Computational Statistics & Data Analysis

Volume:: 54

Issue:: 12

Pages:: 3095-3107

DOI:: 10.1016/j.csda.2009.11.023
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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.

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