A comparison of generalised linear models and compositional models for ordered categorical data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F20%3A73596479" target="_blank" >RIV/61989592:15310/20:73596479 - isvavai.cz</a>
Result on the web
<a href="https://journals.sagepub.com/doi/epub/10.1177/1471082X18816540" target="_blank" >https://journals.sagepub.com/doi/epub/10.1177/1471082X18816540</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1177/1471082X18816540" target="_blank" >10.1177/1471082X18816540</a>
Alternative languages
Result language
angličtina
Original language name
A comparison of generalised linear models and compositional models for ordered categorical data
Original language description
Ordered categorical data occur in many applied fields, such as geochemistry, econometrics, sociology and demography or even transportation research, for example, in the form of results from various questionnaires. There are different possibilities for modelling proportions of individual categories. Generalised linear models (GLMs) are traditionally used for this purpose, but also methods of compositional data analysis (CoDa) can be considered. Here, both approaches are compared in depth. Particularly, different assumptions of the models on variability are highlighted. Advantages and disadvantages of individual models are pointed out. While the CoDa model may be inappropriate when the variability of the compositional coordinates depends on the regressors, for example, due to different total counts on which the coordinates are based, the GLM may underestimate the uncertainty of the predictions considerably in case of large-scale data.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - 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
Result continuities
Project
—
Continuities
N - Vyzkumna aktivita podporovana z neverejnych zdroju
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
STATISTICAL MODELLING
ISSN
1471-082X
e-ISSN
1477-0342
Volume of the periodical
20
Issue of the periodical within the volume
3
Country of publishing house
GB - UNITED KINGDOM
Number of pages
25
Pages from-to
249-273
UT code for WoS article
000532435800002
EID of the result in the Scopus database
2-s2.0-85060626313