Bookcover of Robust Regression Methods for Insurance Risk Classification
Booktitle:

Robust Regression Methods for Insurance Risk Classification

Robust Methods Using Multinomial Logistic Risk Insurance

LAP LAMBERT Academic Publishing (2010-10-20 )

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ISBN-13:

978-3-8383-9928-7

ISBN-10:
3838399285
EAN:
9783838399287
Book language:
English
Blurb/Shorttext:
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.
Publishing house:
LAP LAMBERT Academic Publishing
Website:
http://www.lap-publishing.com/
By (author) :
Esteban Flores
Number of pages:
116
Published on:
2010-10-20
Stock:
Available
Category:
Theory of probability, stochastics, mathematical statistics
Price:
4671.53 руб
Keywords:
Robust Classification, Risk, Insurance, Multinomial Logistic Regression

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