Exploratory tools for outlier detection in compositional data with structural zeros

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F17%3A73582557" target="_blank" >RIV/61989592:15310/17:73582557 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1080/02664763.2016.1182135" target="_blank" >http://dx.doi.org/10.1080/02664763.2016.1182135</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/02664763.2016.1182135" target="_blank" >10.1080/02664763.2016.1182135</a>

Result language

angličtina

Original language name

Exploratory tools for outlier detection in compositional data with structural zeros

Original language description

The analysis of compositional data using the log-ratio approach is based on ratios between the compositional parts. Zeros in the parts thus cause serious difficulties for the analysis. This is a particular problem in case of structural zeros, which cannot be simply replaced by a non-zero value as it is done, e.g. for values below detection limit or missing values. Instead, zeros to be incorporated into further statistical processing. The focus is on exploratory tools for identifying outliers in compositional data sets with structural zeros. For this purpose, Mahalanobis distances are estimated, computed either directly for subcompositions determined by their zero patterns, or by using imputation to improve the efficiency of the estimates, and then proceed to the subcompositional and subgroup level. For this approach, new theory is formulated that allows to estimate covariances for imputed compositional data and to apply estimations on subgroups using parts of this covariance matrix. Moreover, the zero pattern structure is analyzed using principal component analysis for binary data to achieve a comprehensive view of the overall multivariate data structure. The proposed tools are applied to larger compositional data sets from official statistics, where the need for an appropriate treatment of zeros is obvious.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10103 - Statistics and probability

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2017

Confidentiality

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

Name of the periodical

Journal of Applied Statistics

ISSN

0266-4763

e-ISSN

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Volume of the periodical

44

Issue of the periodical within the volume

4

Country of publishing house

GB - UNITED KINGDOM

Number of pages

19

Pages from-to

734-752

UT code for WoS article

000396038500011

EID of the result in the Scopus database

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