Robust Principal Component Analysis for Compositional Tables

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F20%3A73598524" target="_blank" >RIV/61989592:15310/20:73598524 - isvavai.cz</a>

Result on the web

<a href="https://www.tandfonline.com/doi/full/10.1080/02664763.2020.1722078" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/02664763.2020.1722078</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/02664763.2020.1722078" target="_blank" >10.1080/02664763.2020.1722078</a>

Result language

angličtina

Original language name

Robust Principal Component Analysis for Compositional Tables

Original language description

A data table arranged according to two factors can often be considered a compositional table. An example is the number of unemployed people, split according to gender and age classes. Analyzed as compositions, the relevant information consists of ratios between different cells of such a table. This is particularly useful when analyzing several compositional tables jointly, where the absolute numbers are in very different ranges, e.g. if unemployment data are considered from different countries. Within the framework of the logratio methodology, compositional tables can be decomposed into independent and interactive parts, and orthonormal coordinates can be assigned to these parts. However, these coordinates usually require some prior knowledge about the data, and they are not easy to handle for exploring the relationships between the given factors. Here we propose a special choice of coordinates with direct relation to centered logratio (clr) coefficients, which are particularly useful for an interpretation in terms of the original cells of the tables. With these coordinates, robust principal component analysis (rPCA) is performed for dimension reduction, allowing to investigate relationships between the factors. The link between orthonormal coordinates and clr coefficients enables to apply rPCA, which would otherwise suffer from the singularity of clr coefficients.

Czech name

—

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

—

OECD FORD branch

10103 - Statistics and probability

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2020

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

—

Volume of the periodical

48

Issue of the periodical within the volume

2

Country of publishing house

GB - UNITED KINGDOM

Number of pages

20

Pages from-to

214-233

UT code for WoS article

000512574700001

EID of the result in the Scopus database

2-s2.0-85078938090