Imputation of rounded zeros for high-dimensional compositional data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F16%3A33159172" target="_blank" >RIV/61989592:15310/16:33159172 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989592:15110/16:33159172

Výsledek na webu

<a href="http://www.sciencedirect.com/science/article/pii/S0169743916300958" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0169743916300958</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.chemolab.2016.04.011" target="_blank" >10.1016/j.chemolab.2016.04.011</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Imputation of rounded zeros for high-dimensional compositional data

Popis výsledku v původním jazyce

High-dimensional compositional data, multivariate observations carrying relative information, frequently contain values below a detection limit (rounded zeros). We introduce new model-based procedures for replacing these values with reasonable numbers, so that the completed data set is ready for use with statistical analysis methods that rely on complete data, such as regression or classification with high-dimensional explanatory variables. The procedures respect the geometry of compositional data and can be considered as alternatives to existing methods. Simulations show that especially in high-dimensions, the proposed methods outperform existing methods. Moreover, even for a large number of rounded zeros, the new methods lead to an improved quality of the data, which is important for further analyses. The usefulness of the procedure is demonstrated using a data example from metabolomics.

Název v anglickém jazyce

Imputation of rounded zeros for high-dimensional compositional data

Popis výsledku anglicky

High-dimensional compositional data, multivariate observations carrying relative information, frequently contain values below a detection limit (rounded zeros). We introduce new model-based procedures for replacing these values with reasonable numbers, so that the completed data set is ready for use with statistical analysis methods that rely on complete data, such as regression or classification with high-dimensional explanatory variables. The procedures respect the geometry of compositional data and can be considered as alternatives to existing methods. Simulations show that especially in high-dimensions, the proposed methods outperform existing methods. Moreover, even for a large number of rounded zeros, the new methods lead to an improved quality of the data, which is important for further analyses. The usefulness of the procedure is demonstrated using a data example from metabolomics.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BA - Obecná matematika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Chemometrics and Intelligent Laboratory Systems

ISSN

0169-7439

e-ISSN

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Svazek periodika

155

Číslo periodika v rámci svazku

JUL

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

8

Strana od-do

183-190

Kód UT WoS článku

000377732400020

EID výsledku v databázi Scopus

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