Suchergebnisse

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.

National audience ; 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.

Nous évaluons dans quelle mesure nous pouvons prédire l'usage des sols (usage urbain, usage agricole, forêts, prairies et sols naturels) au niveau des points de l'enquête Teruti-Lucas à partir de covariables facilement accessibles. Notre approche comporte deux étapes : la première permet de modéliser l'usage du sol au niveau des points Teruti-Lucas et la deuxième propose une méthode pour en déduire l'utilisation des sols sur un maillage défini par des carreaux. Le modèle de la première étape fournit des prédictions à un niveau fin. La deuxième étape agrège ces prédictions sur les carreaux du maillage en comparant plusieurs méthodes. Nous envisageons différents maillages réguliers du territoire en carreaux pour étudier la qualité de restitution en fonction de la résolution. Nous montrons qu'avec des variables facilement accessibles on obtient une qualité de prédiction acceptable au niveau point et que l'amélioration de la qualité est importante dès la première étape d'agrégation. Classification JEL : C21, C25, C38, Q15, R14.

This book includes a wide selection of the papers presented at the 48th Scientific Meeting of the Italian Statistical Society (SIS2016), held in Salerno on 8-10 June 2016. Covering a wide variety of topics ranging from modern data sources and survey design issues to measuring sustainable development, it provides a comprehensive overview of the current Italian scientific research in the fields of open data and big data in public administration and official statistics, survey sampling, ordinal and symbolic data, statistical models and methods for network data, time series forecasting, spatial analysis, environmental statistics, economic and financial data analysis, statistics in the education system, and sustainable development. Intended for researchers interested in theoretical and empirical issues, this volume provides interesting starting points for further research.