Binary generalized extreme value additive modelling

Silvia Angela Osmetti, Raffaella Calabrese, Giampiero Marra

Risultato della ricerca: Contributo in libroContributo a convegno


Logistic regression is the commonly used model for bankruptcy prediction of small and medium enterprises, for instance. However, the assumptions of symmetric link function and linear or pre-specified covariate-response relationships may not be realistic, especially in scoring applications. To deal with these issues a binary generalized extreme value additive model is introduced. The approach uses the quantile function of the generalized extreme value distribution as link function as well as smooth functions of continuous predictors to flexibly model their effects. The framework is implemented in the bgeva R package which has a bgeva() function that works in a similar way to the glm() and gam()-like functions in R. The main ideas behind the methodology will be discussed and the bgeva package illustrated using Italian data on small and medium enterprises.
Lingua originaleEnglish
Titolo della pubblicazione ospitePROGRAMME AND ABSTRACTS 7th International Conference on Computational and Financial Econometrics (CFE 2013) and 6th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (ERCIM 2013)
Numero di pagine1
Stato di pubblicazionePubblicato - 2013
Evento6th International Conference of the ERCIM WG on Computational and Methodological Statistics (ERCIM 2013) - London
Durata: 14 dic 201316 dic 2013


Convegno6th International Conference of the ERCIM WG on Computational and Methodological Statistics (ERCIM 2013)


  • generalized additive model


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