Robust Regression Methods for Insurance Risk Classification: Robust Methods Using Multinomial Logistic Risk Insurance - Couverture souple

Flores, Esteban

 
9783838399287: Robust Regression Methods for Insurance Risk Classification: Robust Methods Using Multinomial Logistic Risk Insurance
Couverture souple
ISBN 10 : 3838399285 ISBN 13 : 9783838399287
Editeur : LAP LAMBERT Academic Publishing, 2010

Synopsis

Risk classification is an important actuarial process for Insurance companies. It allows for the underwriting of the best risks, through an appropriate choice of classification variables, and helps set fair premiums in rate-making. Currently, insurance companies mainly use ad-hoc methods for risk classification, more often based on the type of expenses covered than on the distribution of the corresponding losses. The selection of classification variables is also, in general, based on rate-making variables rather than on an optimal choice criteria based on statistical methods. It is known that logistic regression is among the many sophisticated statistical methods used by the banking industry in order to select credit rating variables. Extending the method to insurance risks seems only natural. Insurance risks are not usually classified in only two categories, good and bad, as can be the case in credit rating, but in a larger number of classes. Here we consider the generalization of the model to extend the use of logistic regression to insurance risk classification.

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Présentation de l'éditeur

Risk classification is an important actuarial process for Insurance companies. It allows for the underwriting of the best risks, through an appropriate choice of classification variables, and helps set fair premiums in rate-making. Currently, insurance companies mainly use ad-hoc methods for risk classification, more often based on the type of expenses covered than on the distribution of the corresponding losses. The selection of classification variables is also, in general, based on rate-making variables rather than on an optimal choice criteria based on statistical methods. It is known that logistic regression is among the many sophisticated statistical methods used by the banking industry in order to select credit rating variables. Extending the method to insurance risks seems only natural. Insurance risks are not usually classified in only two categories, good and bad, as can be the case in credit rating, but in a larger number of classes. Here we consider the generalization of the model to extend the use of logistic regression to insurance risk classification.

Biographie de l'auteur

completed a Ph.D. degree in Actuarial Mathematics at the Concordia University in 2002. He has been a faculty member of universities in Chile, Canada and Mexico since 1991, where he has developed teaching and research in statistics, risk and insurance. He has also been involved in actuarial practice and financial advisor.

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

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2010
Langue
anglais
ISBN 10 
3838399285
ISBN 13 
9783838399287
Reliure
Broché
Nombre de pages
116
Coordonnées du fabricant
non disponible
Personne responsable
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