Geographically Weighted Regression Analysis for Two-Factorial Compositional Data

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Geographically Weighted Regression Analysis for Two-Factorial Compositional Data

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Geographically Weighted Regression Analysis for Two-Factorial Compositional Data

Popis výsledku anglicky

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.

Druh

C - Kapitola v odborné knize

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA18-05432S" target="_blank" >GA18-05432S: Prostorová syntéza založená na pokročilých metodách geocomputation</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 knihy nebo sborníku

Advances in Compositional Data Analysis, Festschrift in Honour of Vera Pawlowsky-Glahn

ISBN

978-3-030-71174-0

Počet stran výsledku

22

Strana od-do

103-124

Počet stran knihy

404

Název nakladatele

Springer

Místo vydání

Cham

Kód UT WoS kapitoly

—