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Langel, Matti

Nom
Langel, Matti
Affiliation principale
Fonction
Ancien.ne collaborateur.trice
Identifiants
https://libra.unine.ch/handle/123456789/547

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  • Publication
    Métadonnées seulement
    Variance estimation of the Gini index: Revisiting a result several times published
    (2013)
    Langel, Matti 
    ;
    Tillé, Yves 
    Since Corrado Gini suggested the index that bears his name as a way of measuring inequality, the computation of variance of the Gini index has been subject to numerous publications. In this paper, we survey a large part of the literature related to the topic and show that the same results, as well as the same errors, have been republished several times, often with a clear lack of reference to previous work. Whereas existing literature on the subject is very fragmented, we regroup papers from various fields and attempt to bring a wider view of the problem. Moreover, we try to explain how this situation occurred and the main issues involved when trying to perform inference on the Gini index, especially under complex sampling designs. The interest of several linearization methods is discussed and the contribution of recent papers is evaluated. Also, a general result to linearize a quadratic form is given, allowing the approximation of variance to be computed in only a few lines of calculation. Finally, the relevance of the regression-based approach is evaluated and an empirical comparison is proposed.
  • Publication
    Accès libre
    Corrado Gini, a pioneer in balanced sampling and inequality theory
    (2011-3-14)
    Langel, Matti 
    ;
    Tillé, Yves 
    This paper attempts to make the link between two of Corrado Gini’s contributions to statistics: the famous inequality measure that bears his name and his work in the early days of balanced sampling. Some important notions of the history of sampling such as representativeness, randomness, and purposive selection are clarified before balanced sampling is introduced. The Gini index is described, as well as its estimation and variance estimation in the sampling framework. Finally, theoretical grounds and some simulations on real data show how some well used auxiliary information and balanced sampling can enhance the accuracy of the estimation of the Gini index.