Blog Archives

Job for life ? Bishop of Rome ?

February 26, 2013
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Job for life ? Bishop of Rome ?

The job of Bishop of Rome – i.e. the Pope – is considered to be a life-long commitment. I mean, it usually was. There have been 266 popes since 32 A.D. (according to http://oce.catholic.com/…): almost all popes have served until their death. But that does not mean that they were in the job for long… One can easily extract the data from the website, > L2=scan("http://oce.catholic.com/index.php?title=List_of_Popes",what="character") Read 4485 items >…

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Further readings on GLMs and ratemaking

February 21, 2013
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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 Insurance  by Edward Frees, Glenn Meyers and David Cummings, http://variancejournal.org/issues/… see also Predictive Modeling of Multi-Peril Homewoners Insurance Predictive Modeling by Serhat Guven, http://casact.org/newsletter/… “Goodness of Fit” vs. “Goodness of Lift”, by Glenn Meyers and David Cummings, http://casact.org/newsletter/… Pure Premium Regression with the Tweedie Model, by Glenn Meyers, http://casact.org/newsletter/… Beyond…

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Bristish Statisticians and American Gangsters

February 18, 2013
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Bristish Statisticians and American Gangsters

A few months ago, I did publish a post (in French) following my reading of Leonard Mlodinow’s the Drunkard’s Walk. More precisely, I mentioned a paragraph that I found extremely informative But it looks like those gangsters were not only stealing money. They were also stealing ideas, here from a British statistician, manely Leonard Henry Caleb Tippett. Leonard Tippett is famous in Extreme Value Theory for his theorem (the so-called Fisher-Tippett theorem, which gives the…

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Modélisation des coûts individuels en tarification

February 18, 2013
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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 avait été dit dans le cours de modèles de régression sur les modèles log-linéaires, rappelé dans un court billet publié à l’automne). On parlera aussi de mélanges de lois, et de lois multinomiales. Les transparents…

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From Simpson’s paradox to pies

February 16, 2013
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From Simpson’s paradox to pies

Today, I wanted to publish a post on economics, and decision theory. And probability too… Those who do follow my blog should know that I am a big fan of Simpson’s paradox. I also love to mention it in my econometric classes. It does raise important questions, that I do relate to multicolinearity, and interepretations of regression models, with multiple (negatively correlated) explanatory variables. This paradox has amazing pedogological virtues. I did mention it several…

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Visualizing overdispersion (with trees)

February 13, 2013
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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 factor to compute classes. Of course, it is possible to use much more factors. For instance, using cartesian products of factors, > X=as.factor(paste(sinistres$carburant,sinistres$zone, + cut(sinistres$ageconducteur,breaks=c(17,24,40,65,101)))) > E=sinistres$exposition > Y=sinistres$nbre > vm=vv=ve=rep(NA,length(levels(X))) > for(i in 1:length(levels(X))){ + ve[i]=Ei=E[X==levels(X)[i]] + Yi=Y[X==levels(X)[i]] +…

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Large claims, and ratemaking

February 13, 2013
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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 should be considered to model the distribution of the cost , given some covariates .Here is the dataset we’ll use, > sinistre=read.table("http://freakonometrics.free.fr/sinistreACT2040.txt", + header=TRUE,sep=";") > sinistres=sinistre[sinistre$garantie=="1RC",] > sinistres=sinistres[sinistres$cout>0,] > contrat=read.table("http://freakonometrics.free.fr/contractACT2040.txt", + header=TRUE,sep=";") > couts=merge(sinistres,contrat) > tail(couts) nocontrat…

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