Geographically Weighted Regression Analysis for Two-Factorial Compositional Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F21%3A73609682" target="_blank" >RIV/61989592:15310/21:73609682 - isvavai.cz</a>
Result on the web
<a href="https://obd.upol.cz/id_publ/333189568" target="_blank" >https://obd.upol.cz/id_publ/333189568</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-71175-7_6" target="_blank" >10.1007/978-3-030-71175-7_6</a>
Alternative languages
Result language
angličtina
Original language name
Geographically Weighted Regression Analysis for Two-Factorial Compositional Data
Original language description
The chapter focuses on the modelling and analysis of spatial dependent two-factorial compositional data. Spatial statistics provides a wide range of methods for the analysis of data with local variations but only a few of them are accommodated for the purposes of modelling relative structures. In this chapter, the geographically weighted regression model is introduced to analyse the relationship between the dependent variable and an explanatory variable reflecting a structure expressed in terms of a compositional table. The methodology is motivated by the problem of modelling local variations of the relationship between at-risk-of-poverty rates and the structure of the highest attained educational level in the German population aged 30–34. The real data study shows how information included in a compositional table and further expressed in real-valued coordinates can be highly valuable in selecting variables and prioritising them with respect to a research interest to facilitate the final interpretation of the model.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
<a href="/en/project/GA18-05432S" target="_blank" >GA18-05432S: Spatial synthesis based on advanced geocomputation methods</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Advances in Compositional Data Analysis, Festschrift in Honour of Vera Pawlowsky-Glahn
ISBN
978-3-030-71174-0
Number of pages of the result
22
Pages from-to
103-124
Number of pages of the book
404
Publisher name
Springer
Place of publication
Cham
UT code for WoS chapter
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