Identifying important pairwise logratios in compositional data with sparse principal component analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F25%3A73634000" target="_blank" >RIV/61989592:15310/25:73634000 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/62690094:18450/25:50021776

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s11004-024-10159-0" target="_blank" >https://link.springer.com/article/10.1007/s11004-024-10159-0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11004-024-10159-0" target="_blank" >10.1007/s11004-024-10159-0</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Identifying important pairwise logratios in compositional data with sparse principal component analysis

Popis výsledku v původním jazyce

Compositional data are characterized by the fact that their elemental information is contained in simple pairwise logratios of the parts that constitute the composition. While pairwise logratios are typically easy to interpret, the number of possible pairs to consider quickly becomes too large even for medium-sized compositions, which may hinder interpretability in further multivariate analysis. Sparse methods can therefore be useful for identifying a few important pairwise logratios (and parts contained in them) from the total candidate set. To this end, we propose a procedure based on the construction of all possible pairwise logratios and employ sparse principal component analysis to identify important pairwise logratios. The performance of the procedure is demonstrated with both simulated and real-world data. In our empirical analysis, we propose three visual tools showing (i) the balance between sparsity and explained variability, (ii) the stability of the pairwise logratios, and (iii) the importance of the original compositional parts to aid practitioners in their model interpretation.

Název v anglickém jazyce

Identifying important pairwise logratios in compositional data with sparse principal component analysis

Popis výsledku anglicky

Compositional data are characterized by the fact that their elemental information is contained in simple pairwise logratios of the parts that constitute the composition. While pairwise logratios are typically easy to interpret, the number of possible pairs to consider quickly becomes too large even for medium-sized compositions, which may hinder interpretability in further multivariate analysis. Sparse methods can therefore be useful for identifying a few important pairwise logratios (and parts contained in them) from the total candidate set. To this end, we propose a procedure based on the construction of all possible pairwise logratios and employ sparse principal component analysis to identify important pairwise logratios. The performance of the procedure is demonstrated with both simulated and real-world data. In our empirical analysis, we propose three visual tools showing (i) the balance between sparsity and explained variability, (ii) the stability of the pairwise logratios, and (iii) the importance of the original compositional parts to aid practitioners in their model interpretation.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GF22-15684L" target="_blank" >GF22-15684L: Zobecněná relativní data a robustnost v Bayesových prostorech</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Mathematical Geosciences

ISSN

1874-8961

e-ISSN

1874-8953

Svazek periodika

57

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

26

Strana od-do

333-358

Kód UT WoS článku

001329802000001

EID výsledku v databázi Scopus

2-s2.0-85206355324