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

Yuxue Jin

ISBN 10: 3639343190 / ISBN 13: 9783639343199

Published by VDM Verlag Dr. Müller, 2011

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Seller Inventory # 9783639343199

Bibliographic Details

• Title: Regression modeling of competing risks : Applications to bone marrow transplantation studies and mortgage prepayment and default analysis
• Publisher: VDM Verlag Dr. Müller
• Publication Date: 2011
• Language: English
• ISBN 10: 3639343190
• ISBN 13: 9783639343199
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 6D millimeters 220 millimeters width by 150 millimeters height by 6 millimeters depth
• Weight: 176 grams

About this title

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.

About the Author

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.