ECTAP
 
HomeDespre ECTAEventsPolitica editorialaTrimite un articolParteneri / link-uri utileArchiveAbonamentContact
 

ISSN 1841-8678   (print)
ISSN 1844-0029   (online)

News

Archive ECTAP

Note: for the period 1994-2003 the archive of the magazine will not be available online

Supplements ECTAP

If you cannot open the pdf file you need Adobe Reader.
download Adobe Reader

Creative Commons License

Theoretical and Applied Economics
No. 11 / 2008 (528)

Application of Discriminant Analysis on Romanian Insurance Market

Constantin Anghelache
Dan Armeanu
Academy of Economic Studies, Bucharest

Abstract. Discriminant analysis is a supervised learning technique that can be used in order to determine which variables are the best predictors of the classification of objects belonging to a population into predetermined classes. At the same time, discriminant analysis provides a powerful tool that enables researchers to make predictions regarding the classification of new objects into predefined classes. The main goal of discriminant analysis is to determine which of the N descriptive variables have the most discriminatory power, that is, which of them are the most relevant for the classification of objects into classes. In order to classify objects, we need a mathematical model that provides the rules for optimal allocation. This is the classifier. In this paper we will discuss three of the most important models of classification: the Bayesian criterion, the Mahalanobis criterion and the Fisher criterion. In this paper, we will use discriminant analysis to classify the insurance companies that operated on the Romanian market in 2006. We have selected a number of eigth (8) relevant variables: gross written premium (GR_WRI_PRE), net mathematical reserves (NET_M_PES), gross claims paid (GR_CL_PAID), net premium reserves (NET_PRE_RES), net claim reserves (NET_CL_RES), net income (NE—_INCOME), share capital (SHARE_CAP) and gross written premium ceded in Reinsurance (GR_WRI_PRE_CED). Before proceeding to discriminant analysis, we performed cluster analysis on the initial data in order to identify classes (clusters) that emerge from the data.

Keywords: discriminant analysis; classifier; classification cost; prediction Fisher classifier; Bayesian classifier; Mahalanobis classifier; insurance.

Download the full article:  

Contents

Crisis Phoenix
Marin Dinu

Open acces

ECTAP

Search

BOOKS

The Economicity. The Epistemic Landscape, Marin Dinu, 2016

Partners


ISSN 1841-8678 (ediția print) / ISSN 1844-0029 (ediția online)
© Copyright Asociația Generală a Economiștilor din România / Editura Economică
Redacția: Oficiul poștal 18, Ghișeul 3 - Căsuța poștală 31, București 014820, E-mail: economia.ta@edeconomica.com

© 2006-2025 Theoretical and Applied Economics