68 lines
2.3 KiB
Markdown
68 lines
2.3 KiB
Markdown
---
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categories: [literature note, ]
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title: Note de lecture de *Imputation of missing values for compositional data using classical and robust methods* de
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K. Hron, M. Templ, P. Filzmoser.
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bibliography: ../these_ref.bib
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---
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::: {.callout-note title="Synthèse"}
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**Contribution**::
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**Related**::
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:::
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::: {.callout-note title="Markdown"}
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**FirstAuthor**:: Hron, K.
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**Author**:: Templ, M.
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**Author**:: Filzmoser, P.
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**Title**:: Imputation of missing values for compositional data using classical and robust methods
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**Year**:: 2010
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**Citekey**:: hronImputationMissingValues2010
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**itemType**:: journalArticle
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**Journal**:: *Computational Statistics & Data Analysis*
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**Volume**:: 54
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**Issue**:: 12
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**Pages**:: 3095-3107
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**DOI**:: 10.1016/j.csda.2009.11.023
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:::
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::: {.callout-note title="Pièces-jointes"}
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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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:::
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::: {.callout-note title="Abstract"}
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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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::::
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# Prise de notes
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{{< include local_macros.tex.md >}}
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![[local_macros.tex]]
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%% begin user_notes %%
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%% end user_notes %%
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# Annotations importées
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%% begin annotations %%
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%% end annotations %%
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%% Import Date: 2026-05-27T15:46:47.652+02:00 %%
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