Robust regression with compositional covariates including cellwise outliers

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F21%3A73610085" target="_blank" >RIV/61989592:15310/21:73610085 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s11634-021-00436-9" target="_blank" >https://link.springer.com/article/10.1007/s11634-021-00436-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11634-021-00436-9" target="_blank" >10.1007/s11634-021-00436-9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Robust regression with compositional covariates including cellwise outliers

Popis výsledku v původním jazyce

We propose a robust procedure to estimate a linear regression model with compositional and real-valued explanatory variables. The proposed procedure is designed to be robust against individual outlying cells in the data matrix (cellwise outliers), as well as entire outlying observations (rowwise outliers). Cellwise outliers are first filtered and then imputed by robust estimates. Afterwards, rowwise robust compositional regression is performed to obtain model coefficient estimates. Simulations show that the procedure generally outperforms a traditional rowwise-only robust regression method (MM-estimator). Moreover, our procedure yields better or comparable results to recently proposed cellwise robust regression methods (shooting S-estimator, 3-step regression) while it is preferable for interpretation through the use of appropriate coordinate systems for compositional data. An application to bio-environmental data reveals that the proposed procedure—compared to other regression methods—leads to conclusions that are best aligned with established scientific knowledge.

Název v anglickém jazyce

Robust regression with compositional covariates including cellwise outliers

Popis výsledku anglicky

We propose a robust procedure to estimate a linear regression model with compositional and real-valued explanatory variables. The proposed procedure is designed to be robust against individual outlying cells in the data matrix (cellwise outliers), as well as entire outlying observations (rowwise outliers). Cellwise outliers are first filtered and then imputed by robust estimates. Afterwards, rowwise robust compositional regression is performed to obtain model coefficient estimates. Simulations show that the procedure generally outperforms a traditional rowwise-only robust regression method (MM-estimator). Moreover, our procedure yields better or comparable results to recently proposed cellwise robust regression methods (shooting S-estimator, 3-step regression) while it is preferable for interpretation through the use of appropriate coordinate systems for compositional data. An application to bio-environmental data reveals that the proposed procedure—compared to other regression methods—leads to conclusions that are best aligned with established scientific knowledge.

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/GA19-07155S" target="_blank" >GA19-07155S: Identifikace regulačních sítí kontrolujících vývoj osemení hrachu pomocí RNA sekvenování, proteinové a metabolomické analýzy.</a><br>

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Advances in Data Analysis and Classification

ISSN

1862-5347

e-ISSN

—

Svazek periodika

2021

Číslo periodika v rámci svazku

15

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

41

Strana od-do

869-909

Kód UT WoS článku

000621244900001

EID výsledku v databázi Scopus

2-s2.0-85101504633