Generalized Extreme Value Regression: an Application to Credit Defaults
نویسندگان
چکیده
We aim at proposing a Generalized Linear Model (GLM) with binary dependent variable Y , whose link function defined by the Generalized Extreme Value (GEV) distribution. We define this model as GEV regression. The goal of this paper is to overcome the drawbacks shown by the logistic regression in rare events: the probability of rare events is underestimated and the logit link is a symmetric function. Let Y denote a binary response variable and let X be an explanatory variable, the logistic response curve is
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UCD GEARY INSTITUTE DISCUSSION PAPER SERIES Generalized Extreme Value Regression for Binary Rare Events Data: an Application to Credit Defaults
The most used regression model with binary dependent variable is the logistic regression model. When the dependent variable represents a rare event, the logistic regression model shows relevant drawbacks. In order to overcome these drawbacks we propose the Generalized Extreme Value (GEV) regression model. In particular, in a Generalized Linear Model (GLM) with binary dependent variable we sugge...
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