{"product_id":"robust-regression-methods-for-insurance-risk-classification-von-esteban-flores","title":"Robust Regression Methods for Insurance Risk Classification","description":"\u003cp\u003eRisk 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.\u003c\/p\u003e\u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9783838399287\"\u003e\u003ch3\u003eRobust Methods Using Multinomial Logistic Risk Insurance\u003c\/h3\u003e\u003c\/div\u003e","brand":"Autorenwelt Shop","offers":[{"title":"Softcover - 9783838399287","offer_id":39469269188701,"sku":"9783838399287","price":49.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0940\/0622\/files\/87d8134b-272f-40c4-b9ef-ff8c4007f31d.jpg?v=1781587667","url":"https:\/\/shop.autorenwelt.de\/en\/products\/robust-regression-methods-for-insurance-risk-classification-von-esteban-flores","provider":"Autorenwelt Shop","version":"1.0","type":"link"}