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

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**Contribution**::

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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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 - [PDF](file:///home/louis/snap/zotero-snap/common/Zotero/storage/FR4TF52C/Hron%20et%20al.%20-%202010%20-%20Imputation%20of%20missing%20values%20for%20compositional%20data%20using%20classical%20and%20robust%20methods.pdf).
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::: {.callout-note title="Abstract"}

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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# Prise de notes

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![[local_macros.tex]]

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