1,552 research outputs found
A Generalized Additive Model for Binary Rare Events Data: an Application to Credit Defaults.
No full textWe aim at proposing a new model for binary rare events, i.e. binary depen-
dent variable with a very small number of ones.We extend the Generalized Extreme
Value (GEV) regression model proposed by Calabrese and Osmetti [5] to a Gener-
alized Additive Model (GAM). We suggest to consider the quantile function of the
GEV distribution as a link function in a GAM, so we propose the Generalized Extreme Value Additive (GEVA) model. In order to estimate the GEVA model, a modified version of the local scoring algorithm of GAM is proposed. Finally, to model
default probability, we apply our proposal to empirical data on Italian Small and
Medium Enterprises (SMEs). The results show that the GEVA model has a higher
predictive accuracy to identify the rare event than the logistic additive model
Improving Forecast of Binary Rare Events Data: A GAM‐Based Approach
No full textThis 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. Copyright © 2015 John Wiley & Sons, Ltd.</jats:p
Default prediction of SMEs by a generalized extreme value additive model
No full textA new model is proposed for default prediction of Small and Medium Enterprises (SMEs). The main weakness of the scoring models proposed in the literature is not to consider the default as a rare event. To take into account this characteristic, Calabrese and Osmetti (2011) suggested the quantile function of the Generalized Extreme Value (GEV) distribution as a link function in a Generalized Linear Model (GLMs). In the GLMs, the relationship between the independent variable and the predictor is constrained to be linear.
Since this assumption is not usually satisfied by scoring models, a Generalized Additive Model (GAM) is suggested with the quantile function of the GEV distribution as link function. Hence, the Generalized Extreme Value Additive (GEVA) model is proposed. Finally, our proposal is applied to empirical data on Italian SMEs. It is obtained that the GEVA model shows a high accuracy for predicting defaults
A GENERALIZED ADDITIVE MODEL FOR BINARY RARE EVENTS DATA: AN APPLICATION TO CREDIT DEFAULTS
No full textWe aim at proposing a new model for binary rare events, i.e. binary dependent
variable with a very small number of ones. We extend the Generalized
Extreme Value (GEV) regression model (Calabrese and Osmetti, 2011) to a Generalized
Additive Model (GAM). We suggest to consider the quantile function of
the GEV distribution as a link function in a GAM, so we propose the Generalized
Extreme Value Additive (GEVA) model. In order to estimate the GEVA model, a
modified version of the local scoring algorithm of GAM is proposed. Finally, to
model default probability, we apply our proposal to empirical data on Italian Small
and Medium Enterprises (SMEs). We obtain that the GEVA model shows a high
predictive accuracy to identify the rare event
Modelling small and medium enterprise loan defaults as rare events: the generalized extreme value regression model
No full textA pivotal characteristic of credit defaults that is ignored by most credit scoring models is the rarity of
the event. The most widely used model to estimate the probability of default is the logistic regression
model. Since the dependent variable represents a rare event, the logistic regression model shows relevant
drawbacks, for example, underestimation of the default probability, which could be very risky for banks.
In order to overcome these drawbacks, we propose the generalized extreme value regression model. In
particular, in a generalized linear model (GLM) with the binary-dependent variable we suggest the quantile
function of the GEV distribution as link function, so our attention is focused on the tail of the response
curve for values close to one. The estimation procedure used is the maximum-likelihood method. This
model accommodates skewness and it presents a generalisation of GLMs with complementary log–log
link function. We analyse its performance by simulation studies. Finally, we apply the proposed model to
empirical data on Italian small and medium enterprises
Dafault Prediction of SMEs by Generalized Extreme Value Additive Model
No full textWe aim at proposing a Generalized Additive Model (GAM) for Small and Medium Enterprises (SMEs). The
Generalized Extreme Value regression model (Calabrese and Osmetti, 2011) is extended by replacing the
linear predictor with an additive one, defined as the sum of arbitrary smooth functions. In order to focus the
attention on the tail of the response curve for values close to one, we consider the quantile function of the
generalized extreme value distribution as a link function in a GAM. Thus we propose the Generalized
Extreme Value Additive (GEVA) model. To estimate the smooth functions, the local scoring algorithm
(Hastie and Tibshirani, 1986) is applied. In credit risk analysis a pivotal topic is the default probability
estimation for SMEs. For this reason, we apply the GEVA regression to empirical data on Italian Small and
Medium Enterprises (SMEs). On this dataset we compare the performance of the GEVA model with the one
of the logistic additive model. The main advantage of the GEVA model is its excellent performance to
identify defaults for low default portfolio. Thanks to this characteristic, the drawback of the logistic
(additive) regression model in underestimating the default probability (King and Zeng, 2001) is overcome.
Finally, the GEVA model is a robust model, unlike the logistic (additive) regression model, if the sample
percentage of defaults is different from that in the out-of-sample analysis
Author Correction: Gluten consumption and inflammation affect the development of celiac disease in at-risk children
No full textThe original version of this Article contained an error in the spelling of the authors Renata Auricchio, Ilaria Calabrese, Martina Galatola, Donatella Cielo, Fortunata Carbone, Marianna Mancuso, Giuseppe Matarese, Riccardo Troncone, Salvatore Auricchio & Luigi Greco which were incorrectly given as Auricchio Renata, Calabrese Ilaria, Galatola Martina, Cielo Donatella, Carbone Fortunata, Mancuso Marianna, Matarese Giuseppe, Troncone Riccardo, Auricchio Salvatore & Greco Luigi. The original article has been corrected
Construction and psychometric properties of the sustainable behavior questionnaire among Italian adults
No full textThis study aimed to develop and validate the Sustainable Behavior Questionnaire (SBQ), a comprehensive tool for assessing environmentally sustainable behaviors among Italian adults. Drawing on established theories and scales, the SBQ was refined through a two-part study. Study 1 involved reviewing the literature, selecting items, and conducting exploratory factor analysis (EFA) on responses from 219 participants. This process resulted in a four-factor structure: “purchase behavior and awareness,” “reusing and recycling,” “reducing,” and “walking” and “use of public transport.” Study 2, comprising 432 participants, confirmed the four-factor structure through confirmatory factor analysis (CFA) and established the SBQ's convergent validity with Intention Sustainable Behavior (ISBQ) and Environment Sustainable Value and Norms (ESVN) measures. The SBQ demonstrated robust internal consistency, providing a reliable instrument for assessing sustainable behaviors. This research contributes to the field by offering a psychometrically sound questionnaire for evaluating sustainable behaviors among Italian adults and further understanding their role in promoting environmental conservation and well-being
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