Regression modeling of competing risks: Applications to bone marrow transplantation studies and mortgage prepayment and default analysis - Couverture souple

Jin, Yuxue

 
9783639343199: Regression modeling of competing risks: Applications to bone marrow transplantation studies and mortgage prepayment and default analysis
Couverture souple
ISBN 10 : 3639343190 ISBN 13 : 9783639343199
Editeur : VDM Verlag Dr. Müller, 2011

Synopsis

Competing risks frequently arise in medical applications when the subject under study may fail from more than one cause. Typically, regression models for competing risks are based on cause-specific hazards. However, the cause-specific hazard model does not give a direct interpretation in terms of the marginal survival probability of a particular failure type. In the first part of this thesis, an iterative maximum likelihood method was proposed to directly model the cumulative incidence function. Competing risks also arise in mortgage data, which involves two mutually exclusive endpoints, prepayment and default. A quantitative model to accurately predict the mortgage prepayment and default rates based on the loan level information and the state of the economy is therefore very important for both risk management and pricing mortgage-backed securities. In the second part of this thesis, we propose a neural network model to model the prepayment and default probabilities.

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Présentation de l'éditeur

Competing risks frequently arise in medical applications when the subject under study may fail from more than one cause. Typically, regression models for competing risks are based on cause-specific hazards. However, the cause-specific hazard model does not give a direct interpretation in terms of the marginal survival probability of a particular failure type. In the first part of this thesis, an iterative maximum likelihood method was proposed to directly model the cumulative incidence function. Competing risks also arise in mortgage data, which involves two mutually exclusive endpoints, prepayment and default. A quantitative model to accurately predict the mortgage prepayment and default rates based on the loan level information and the state of the economy is therefore very important for both risk management and pricing mortgage-backed securities. In the second part of this thesis, we propose a neural network model to model the prepayment and default probabilities.

Biographie de l'auteur

Yuxue Jin, Ph.D., is currently a Post Doctoral Fellow at Eisai Medical Research, Inc. She graduated from Stanford University with a Ph.D. in Statistics and M.S. in Financial Mathematics.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
VDM Verlag Dr. Müller
Date d'édition
2011
Langue
anglais
ISBN 10 
3639343190
ISBN 13 
9783639343199
Reliure
Broché
Nombre de pages
120
Coordonnées du fabricant
non disponible
Personne responsable
non disponible