Modern Actuarial Risk Theory Using R /

Modern Actuarial Risk Theory contains what every actuary needs to know about non-life insurance mathematics. It starts with the standard material like utility theory, individual and collective model and basic ruin theory. Other topics are risk measures and premium principles, bonus-malus systems, or...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Kaas, Rob (Συγγραφέας), Goovaerts, Marc (Συγγραφέας), Dhaene, Jan (Συγγραφέας), Denuit, Michel (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.
Έκδοση:2.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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100 1 |a Kaas, Rob.  |e author. 
245 1 0 |a Modern Actuarial Risk Theory  |h [electronic resource] :  |b Using R /  |c by Rob Kaas, Marc Goovaerts, Jan Dhaene, Michel Denuit. 
250 |a 2. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg,  |c 2008. 
300 |a XVIII, 382 p.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
505 0 |a Utility theory and insurance -- The individual risk model -- Collective risk models -- Ruin theory -- Premium principles and risk measures -- Bonus-malus systems -- Ordering of risks -- Credibility theory -- Generalized linear models -- IBNR techniques -- More on GLMs -- The 'R' in Modern ART -- Hints for the exercises -- Notes and references. 
520 |a Modern Actuarial Risk Theory contains what every actuary needs to know about non-life insurance mathematics. It starts with the standard material like utility theory, individual and collective model and basic ruin theory. Other topics are risk measures and premium principles, bonus-malus systems, ordering of risks and credibility theory. It also contains some chapters about Generalized Linear Models, applied to rating and IBNR problems. As to the level of the mathematics, the book would fit in a bachelors or masters program in quantitative economics or mathematical statistics. This second and much expanded edition emphasizes the implementation of these techniques through the use of R. This free but incredibly powerful software is rapidly developing into the de facto standard for statistical computation, not just in academic circles but also in practice. With R, one can do simulations, find maximum likelihood estimators, compute distributions by inverting transforms, and much more. . 
650 0 |a Finance. 
650 0 |a Business mathematics. 
650 0 |a Applied mathematics. 
650 0 |a Engineering mathematics. 
650 0 |a Economics, Mathematical. 
650 0 |a Actuarial science. 
650 0 |a Statistics. 
650 1 4 |a Finance. 
650 2 4 |a Finance, general. 
650 2 4 |a Applications of Mathematics. 
650 2 4 |a Business Mathematics. 
650 2 4 |a Actuarial Sciences. 
650 2 4 |a Statistics for Business/Economics/Mathematical Finance/Insurance. 
650 2 4 |a Quantitative Finance. 
700 1 |a Goovaerts, Marc.  |e author. 
700 1 |a Dhaene, Jan.  |e author. 
700 1 |a Denuit, Michel.  |e author. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783540709923 
856 4 0 |u http://dx.doi.org/10.1007/978-3-540-70998-5  |z Full Text via HEAL-Link 
912 |a ZDB-2-SBE 
950 |a Business and Economics (Springer-11643)