Mathematical and Statistical Methods for Actuarial Sciences and Finance

(Nora) #1

82 P. Cerchiello, M. Iannario, and D. Piccolo


Men without serious injuriesMen with serious injuries
Women without serious injuries
Women with serious injuries

Expected rating

Working years

2.8 3.2 3.6 4.0 4.40 4 8 12 16 20 24 28 32

Fig. 2.Expected score as a function of working years, given gender and serious injury

suitable and flexible tool for examining and quantifying the change of response over
one or more categorical and/or continuous covariates, and they provide a deeper in-
sight into these kinds of dataset. They also allowed the summary of much information
and some interesting evaluation of specific points investigated when covariates are
both absent and present.
Moreover, we stress that the proposed model is a manifold target approach: in fact,
it can be profitably applied to a variety of fields, ranging from credit and operational
risk [3] to reputational and churn risk. Finally, it represents a convincing tool to exploit
opinions expressed by field experts.


Acknowledgement.Methodological issues and related software are partially supported by the
2006 PRIN-MIUR research project: “Stima e verifica di modelli statistici per l’analisi della
soddisfazione degli studenti universitari”.


References



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  3. Bonafede, E.C., Cerchiello, P., Giudici, P.: Statistical models for Business Continuity
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  4. Bonafede, E.C., Giudici, P.: Bayesian networks for enterprise risk assessment. Phys. A
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  5. Bromiley, P., Curley, S.P.: Individual differences in risk taking. In: Yates, F. (ed.) Risk-
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