An analysis of cause of loss in comprehensive insurance under competing risks regression model
Yıl 2018,
Cilt: 11 Sayı: 1, 1 - 12, 29.06.2018
Murat Kırkağaç
Durdu Karasoy
Öz
The purpose underlying
the main aim of the present study is to apply the competing risks of survival
analysis upon the comprehensive insurance practices, which -in itself- is a
non-life insurance type. To serve for the purpose, a detailed insurance data
set comprising seven explanatory variables that encompass 98,667 observations
was taken into consideration. This data set stemmed from 2014 and was obtained
from a private non-life insurance company. Factors that determine
unsuccessfulness were measured by means of competing risk regression model. For
each probable cause, estimates of cumulative incidence function were obtained
separately. Moreover, by employing competing risks, the comparative effectiveness
of causes had been investigated which would result in creating appropriate
damages.
Kaynakça
-
[1] D. Karasoy, N. A. Tutkun, 2016, Yaşam Çzöümlemesi, Ankara: Nobel Press.
-
[2] M. Pintilie, 2006, Competing risks: a practical perspective (Vol. 58), John Wiley & Sons.
-
[3] M. L. Moeschberger, H. A. David, 1971, Life tests under competing causes of failure and the theory of competing risks, Biometrics 4, 909-933.
-
[4] J. F. Carriere, 1994, Dependent decrement theory, Transactions of the Society of Actuaries, 46, 45-74.
-
[5] A.S. MacDonald, 1997, An Actuarial Survey of Statistical Models for Decrement and Transition Data II: Competing Risks, Non-Parametric and Regression Models. Insurance Mathematics and Economics, 2(20), 142.
-
[6] E.W. Frees, E. A. Valdez, 1998, Understanding relationships using copulas, North American Actuarial Journal 2, 1-25.
-
[7] N. Keiding, C. Andersen, P. Fledelius, 1998, The Cox Regression Model for Claims Data m Non-Life Insurance. Astin Bulletin 28, 95-118.
-
[8] G. L. Grunkemeier, R. Jin, M. J. Eijkemans, J. J. Takkenberg, 2007, Actual and actuarial probabilities of competing risks: apples and lemons, The Annals of Thoracic Surgery, 83, 1586-1592.
-
[9] V. K. Kaishev, D. S. Dimitrova, S. Haberman, 2007, Modelling the joint distribution of competing risks survival times using copula functions, Insurance: Mathematics and Economics, 41, 339-361.
-
[10] J. J. Dignam, Q. Zhang, M.N. Kocherginsky, 2012, The use and interpretation of competing risks regression models. Clinical Cancer Research, clincanres-2097.
-
[11] F. Planchet, J. Tomas, 2016, Uncertainty on survival probabilities and solvency capital requirement: application to long-term care insurance. Scandinavian Actuarial Journal, 2016, 279-292.
-
[12] M. Pintilie, 2011, An introduction to competing risks analysis, Revista Española de Cardiología (English Edition), 64, 599-605.
-
[13] R.G. Gutierrez, 2010, Competing-risks regression, Stata Conference, Boston.
-
[14] B. Haller, G. Schmidt, K. Ulm, 2013, Applying competing risks regression models: an overview. Lifetime data analysis, 19(1), 33-58.
Kasko sigortasında hasar nedenlerinin yarışan riskler regresyon modeli ile analizi
Yıl 2018,
Cilt: 11 Sayı: 1, 1 - 12, 29.06.2018
Murat Kırkağaç
Durdu Karasoy
Öz
Bu çalışmanın
altında yatan temel amaç; yaşam çözümlemesinde yarışan riskleri, aslında bir
hayat dışı sigorta türü olan kasko sigortasına uygulamaktır. Bu amaca
hizmet etmek için, 98.667 tane gözlemi kapsayan yedi tane açıklayıcı değişkeni
içeren kapsamlı bir sigorta veri seti göz önünde bulundurulmuştur. Bu veri seti
2014 yılından gelmiş olup, özel bir hayat dışı sigorta şirketinden alınmıştır. Başarısızlığı
etkileyen faktörler yarışan riskler regresyon modeli aracılığıyla ölçülmüştür.
Birikimli etki fonksiyonunun tahminleri, her olası neden için ayrı ayrı elde
edilmiştir. Buna ek olarak, nedenlerin karşılaştırmalı etkinliği uygun
hasarların oluşmasıyla sonuçlanacak şekilde, yarışan riskler kullanılarak incelenmiştir.
Kaynakça
-
[1] D. Karasoy, N. A. Tutkun, 2016, Yaşam Çzöümlemesi, Ankara: Nobel Press.
-
[2] M. Pintilie, 2006, Competing risks: a practical perspective (Vol. 58), John Wiley & Sons.
-
[3] M. L. Moeschberger, H. A. David, 1971, Life tests under competing causes of failure and the theory of competing risks, Biometrics 4, 909-933.
-
[4] J. F. Carriere, 1994, Dependent decrement theory, Transactions of the Society of Actuaries, 46, 45-74.
-
[5] A.S. MacDonald, 1997, An Actuarial Survey of Statistical Models for Decrement and Transition Data II: Competing Risks, Non-Parametric and Regression Models. Insurance Mathematics and Economics, 2(20), 142.
-
[6] E.W. Frees, E. A. Valdez, 1998, Understanding relationships using copulas, North American Actuarial Journal 2, 1-25.
-
[7] N. Keiding, C. Andersen, P. Fledelius, 1998, The Cox Regression Model for Claims Data m Non-Life Insurance. Astin Bulletin 28, 95-118.
-
[8] G. L. Grunkemeier, R. Jin, M. J. Eijkemans, J. J. Takkenberg, 2007, Actual and actuarial probabilities of competing risks: apples and lemons, The Annals of Thoracic Surgery, 83, 1586-1592.
-
[9] V. K. Kaishev, D. S. Dimitrova, S. Haberman, 2007, Modelling the joint distribution of competing risks survival times using copula functions, Insurance: Mathematics and Economics, 41, 339-361.
-
[10] J. J. Dignam, Q. Zhang, M.N. Kocherginsky, 2012, The use and interpretation of competing risks regression models. Clinical Cancer Research, clincanres-2097.
-
[11] F. Planchet, J. Tomas, 2016, Uncertainty on survival probabilities and solvency capital requirement: application to long-term care insurance. Scandinavian Actuarial Journal, 2016, 279-292.
-
[12] M. Pintilie, 2011, An introduction to competing risks analysis, Revista Española de Cardiología (English Edition), 64, 599-605.
-
[13] R.G. Gutierrez, 2010, Competing-risks regression, Stata Conference, Boston.
-
[14] B. Haller, G. Schmidt, K. Ulm, 2013, Applying competing risks regression models: an overview. Lifetime data analysis, 19(1), 33-58.