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Segmentation en tarification, compléments

Dans le premier cours d’actuariat IARD, nous avons vu l’importance de la ségmentation, et son implication sur le calcul des primes (passer d’une espérance mathématique à une espérance conditionnelle)....

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R for actuarial science

As mentioned in the Appendix of Modern Actuarial Risk Theory, “R (and S) is the ‘lingua franca’ of data analysis and statistical computing, used in academia, climate research, computer science,...

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Régression logistique et arbres

Pour le cours de mercredi prochain, la base utilisée sera une base tirée du livre de Jed Frees, http://instruction.bus.wisc.edu/jfrees/… >...

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Régression de Poisson, et biais minimal

Lors du prochain cours d’actuariat, nous allons finir les arbres de régression, et introduire la régression de Poisson. Les transparents sont en ligne ici, Je vais présenter la régression en Poisson,...

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Regression tree using Gini’s index

In order to illustrate the construction of regression tree (using the CART methodology), consider the following simulated dataset, > set.seed(1) > n=200 > X1=runif(n) > X2=runif(n) >...

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The law of small numbers

In insurance, the law of large numbers (named loi des grands nombres initially by Siméon Poisson, see e.g. http://en.wikipedia.org/…) is usually mentioned to legitimate large portfolios, because of...

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Introduction aux modèles linéaires généralisés

J’ai un peu d’avance dans le cours. Je vais mettre en ligne les transparents pour la semaine prochaine (normalement), où nous aborderons la classe des modèles linéaires généralisés. Les transparents...

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Base de données de tarification

Pour compléter le cours de ce matin, un mot rapide sur les bases, et plus particulièrement la base de contrats. Au sujet des variables, densite est la densité de population dans la commune où habite le...

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Overdispersion with different exposures

In actuarial science, and insurance ratemaking, taking into account the exposure can be a nightmare (in datasets, some clients have been here for a few years – we call that exposure – while others have...

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Fréquence de sinistres, et surdispersion

Je continue à mettre en ligne les transparents qui serviront de support pour le cours ACT2040. Dans cette dernière partie sur la modélisation de la fréquence de sinistre, on parlera de surdispersion....

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Natura non facit saltus

(see John Wilkins’ article on the – interesting – history of that phrase http://scienceblogs.com/evolvingthoughts/…). We will see, this week in class, several smoothing techniques, for insurance...

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Large claims, and ratemaking

During the course, we have seen that it is natural to assume that not only the individual claims frequency can be explained by some covariates, but individual costs too. Of course, appropriate families...

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Visualizing overdispersion (with trees)

This week, we started to discuss overdispersion when modeling claims frequency. In my previous post, I discussed computations of empirical variances with different exposure. But I did use only one...

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Modeling individual losses with mixtures

Usually, the sentence that I keep saying in my regression classes is “please, look at your data“. In our previous post, we’ve been playing like most econometricians: we did not look at the data....

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Modélisation des coûts individuels en tarification

Avant de terminer le cours sur la tarification, on va parler de la modélisation des coûts individuels. On parlera de lois Gamma et de lois lognormales (sur cette dernière, je suggère de relire ce qui...

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Further readings on GLMs and ratemaking

Some articles found in Actuarial journal, on ratemarking, Predictive Modeling—You Mean Actuarial Wizardry?, by Shane Barnes, http://casact.org/newsletter/… Predictive Modeling of Multi-Peril Homeowners...

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Semaine de relâche et données de comptage

Comme annoncé en cours (pour ceux qui souhaitent profiter de la semaine de relâche pour se préparer) une partie de l’examen intra portera sur la base >...

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Données pour la régression logistique, et de Poisson

Pour le cours de mercredi, deux petites bases, pour se pratiquer à modéliser des variable 0/1 ou une variable de comptage, > base = read.table("http://freakonometrics.free.fr/base-glm-act2040.txt",...

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Readings on IBNR and claims reserving

The second part of the course on nonlife insurance will be dedicated to IBNR and claims reserving techniques. The main reference is the textbook by Mario Wüthrich and Michael Merz (a preliminary...

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Multiple (smoothed) regression and portfolio exposure

Wednesday, in class, we’ve seen how to visualize a multiple regression model (with two continuous explanatory variables). Here, the goal is to predict the average cost of an insurance claim, using some...

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