Linear regression with compositional explanatory variables

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F12%3A33141575" target="_blank" >RIV/61989592:15310/12:33141575 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1080/02664763.2011.644268" target="_blank" >http://dx.doi.org/10.1080/02664763.2011.644268</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/02664763.2011.644268" target="_blank" >10.1080/02664763.2011.644268</a>

Result language

angličtina

Original language name

Linear regression with compositional explanatory variables

Original language description

Compositional explanatory variables should not be directly used in a linear regression model because any inference statistic can become misleading. While various approaches for this problem were proposed, here an approach based on the isometric logratio(ilr) transformation is used. It turns out that the resulting model is easy to handle, and that parameter estimation can be done in like in usual linear regression. Moreover, it is possible to use the ilr variables for inference statistics in order to obtain an appropriate interpretation of the model.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

BB - Applied statistics, operational research

OECD FORD branch

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Project

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Continuities

Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2012

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

39

Issue of the periodical within the volume

5

Country of publishing house

GB - UNITED KINGDOM

Number of pages

14

Pages from-to

1115-1128

UT code for WoS article

000304428500012

EID of the result in the Scopus database

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