Literature DB >> 35707505

Analyzing the impacts of socio-economic factors on French departmental elections with CoDa methods.

T H A Nguyen1,2, T Laurent1, C Thomas-Agnan1, A Ruiz-Gazen1.   

Abstract

The vote shares by party on a given subdivision of a territory form a vector called composition (mathematically, a vector belonging to a simplex). It is interesting to model these shares and study the impact of the characteristics of the territorial units on the outcome of the elections. In the political economy literature, few regression models are adapted to the case of more than two political parties. In the statistical literature, there are regression models adapted to share vectors including Compositional Data (CoDa) models, but also Dirichlet models, and others. Our goal is to discuss and illustrate the use CoDa regression models for political economy models for more than two parties. The models are fitted on French electoral data of the 2015 departmental elections.
© 2020 Informa UK Limited, trading as Taylor & Francis Group.

Entities:  

Keywords:  French departmental election; Gaussian distribution; Political economy; compositional regression models; multiparty; vote shares

Year:  2020        PMID: 35707505      PMCID: PMC9041641          DOI: 10.1080/02664763.2020.1858274

Source DB:  PubMed          Journal:  J Appl Stat        ISSN: 0266-4763            Impact factor:   1.416


  2 in total

1.  Relations between socio-economic factors and nutritional diet in Vietnam from 2004 to 2014: New insights using compositional data analysis.

Authors:  Huong Thi Trinh; Joanna Morais; Christine Thomas-Agnan; Michel Simioni
Journal:  Stat Methods Med Res       Date:  2018-04-23       Impact factor: 3.021

2.  Compositional data analysis in epidemiology.

Authors:  Mehmet C Mert; Peter Filzmoser; Gottfried Endel; Ingrid Wilbacher
Journal:  Stat Methods Med Res       Date:  2016-10-06       Impact factor: 3.021

  2 in total

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