Binary generalized extreme value additive modelling

Silvia Angela Osmetti, Raffaella Calabrese, Giampiero Marra

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationPROGRAMME 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)
Number of pages1
Publication statusPublished - 2013
Event6th International Conference of the ERCIM WG on Computational and Methodological Statistics (ERCIM 2013) - London
Duration: 14 Dec 201316 Dec 2013


Conference6th International Conference of the ERCIM WG on Computational and Methodological Statistics (ERCIM 2013)


  • generalized additive model


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