TY - JOUR
T1 - Improving Forecast of Binary Rare Events Data: A GAM-Based Approach
AU - Calabrese, Raffaella
AU - Osmetti, Silvia Angela
PY - 2015
Y1 - 2015
N2 - This paper develops a method for modelling binary response data in a regression model with highly unbalanced class sizes. When the class sizes are highly unbalanced and the minority class represents a rare event, conventional regression analysis, i.e. logistic regression models, could underestimate the probability of the rare event. To overcome this drawback, we introduce a flexible skewed link function based on the quantile function of the generalized extreme value (GEV) distribution in a generalized additive model (GAM). The proposed model is known as generalized extreme value additive (GEVA) regression model, and a modified version of the local scoring algorithm is suggested to estimate it. We apply the proposed model to a dataset on Italian small and medium enterprises (SMEs) to estimate the default probability of SMEs. Our proposal performs better than the logistic (linear or additive) model in terms of predictive accuracy
AB - This paper develops a method for modelling binary response data in a regression model with highly unbalanced class sizes. When the class sizes are highly unbalanced and the minority class represents a rare event, conventional regression analysis, i.e. logistic regression models, could underestimate the probability of the rare event. To overcome this drawback, we introduce a flexible skewed link function based on the quantile function of the generalized extreme value (GEV) distribution in a generalized additive model (GAM). The proposed model is known as generalized extreme value additive (GEVA) regression model, and a modified version of the local scoring algorithm is suggested to estimate it. We apply the proposed model to a dataset on Italian small and medium enterprises (SMEs) to estimate the default probability of SMEs. Our proposal performs better than the logistic (linear or additive) model in terms of predictive accuracy
KW - generalised extreme value distribution
KW - generalized additive model
KW - local scoring algorithm
KW - rare event
KW - generalised extreme value distribution
KW - generalized additive model
KW - local scoring algorithm
KW - rare event
UR - http://hdl.handle.net/10807/66139
U2 - 10.1002/for.2335
DO - 10.1002/for.2335
M3 - Article
SN - 0277-6693
VL - 34
SP - 230
EP - 239
JO - Journal of Forecasting
JF - Journal of Forecasting
ER -