Tag Archives: Exposure

Modelling Occurence of Events, with some Exposure

This afternoon, an interesting point was raised, and I wanted to get back on it (since I did publish a post on that same topic a long time ago). How can we adapt a logistic regression when all the observations do not have the same exposure. Here the model is the following: ,

  • the occurence of an event https://latex.codecogs.com/gif.latex?Y_i^\star on the period https://latex.codecogs.com/gif.latex?[0,1] is unobserved
  • the occurence of an event https://latex.codecogs.com/gif.latex?Y_i on https://latex.codecogs.com/gif.latex?[0,E_i] is observed (as well as https://latex.codecogs.com/gif.latex?E_i)

If we assume that the ‘occurence of an event’ is the first occurence of a Poisson processus, we can prove that

i.e. no event occur on  if no event occur on  and no event occur on . Assuming independence between the two, we can prove that we have

https://latex.codecogs.com/gif.latex?\mathbb{P}(Y=0)%20=%20\mathbb{P}(N=0)^E

With words, it means that the probability of not having a claim in the first six months of the year is the square root of not have a claim over a year. Which makes sense.

Continue reading Modelling Occurence of Events, with some Exposure

Exposure as a possible explanatory variable

Iin insurance pricing, the exposure is usually used as an offset variable to model claims frequency. As explained many times on this blog (e.g. here), and in my notes, if we have to identical drivers, but one with an exposure of 6 months, and the other one of one year, it should be natural to assume that, on average, the second driver will have two times more accidents. This is the motivation to use a standard (homogeneous) Poisson process to model claim frequency. One can also see here legal issue, since, in case of a (partial) reinbursement of a premium, it would be done prorata temporis. The risk is proportional to the exposure. Thus, if https://latex.codecogs.com/gif.latex?Y_i denote the number of claims of insured https://latex.codecogs.com/gif.latex?i, with characteristics https://latex.codecogs.com/gif.latex?\boldsymbol{X}_{i}=(X_{i,1},\cdots,X_{i,k}) and exposure https://latex.codecogs.com/gif.latex?E_i, with a Poisson regression, we would write

https://latex.codecogs.com/gif.latex?Y_i\sim\mathcal{P}(E_i\cdot%20\exp(\boldsymbol{X}_i%27\boldsymbol{\beta}))

or equivalently

https://latex.codecogs.com/gif.latex?Y_i\sim\mathcal{P}(\exp(\log(E_i)+\boldsymbol{X}_i%27\boldsymbol{\beta}))

From this expression, the logarithm of the exposure is an explanatory variable, but there should be no coefficient (the coefficient here is taken to be one). Can’t we use the exposure as an explanatory variable ? Will we get a unit parameter ?

Of course, in the context of ratemaking, it is probably not a relevant question, since actuaries are required to predict annual claim frequency (since insurance contract are supposed to provide a one year coverage). But it might be interesting to get a better understanding of why people might be leaving our portfolio (i.e. are cancelling their insurance policy before term, or not renew someday).

To be more specific and get a better understanding, consider the following model: consider a Poisson process to model claims arrival, and people dedicated to their insurance company (they never leave). Let us generate scenarios over twenty years

> n=983
> D1=as.Date("01/01/1993",'%d/%m/%Y')
> D2=as.Date("31/12/2013",'%d/%m/%Y')
> L=D1+0:(D2-D1)
> set.seed(1)
> arrival=sample(L,size=n,replace=TRUE)
> exposure=N=rep(NA,n)
> departure=rep(D2,n)
> set.seed(2)
> for(i in 1:n){
+   expo=D2-arrival[i]
+   w=0
+   while(max(w)<expo) w=c(w,max(w)+1+trunc(rexp(1,1/1000)))
+   exposure[i]=departure[i]-arrival[i]
+   N[i]=max(0,length(w)-2)}
> df=data.frame(N=N,E=exposure/365)

Here the expected time between claims is considered to be 1000 days. The (annual) intensity of the Poisson process is here

> 365/1000
[1] 0.365

so if we run a Poisson regression on the logarithm of the exposure (please feel free to had other covariates if you want, the example here is just to see what could happen when exposure is considered as a standard covariate), we should get a parameter close to

> log(365/1000)
[1] -1.007858

Here, the regression on a constant, with the offset variable is

> reg=glm(N~1+offset(log(E)),data=df,family=poisson)
> summary(reg)

Call:
glm(formula = N ~ 1 + offset(log(E)), family = poisson, data = df)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.4145  -0.4673   0.2367   0.8770   3.6828  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.04233    0.02532  -41.17   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 1116.9  on 982  degrees of freedom
Residual deviance: 1116.9  on 982  degrees of freedom
AIC: 3282.9

Number of Fisher Scoring iterations: 5

which is consistent with what we just said. If we run the regression with the logarithm of the exposure as a possible explanatory variable, we would expect to have a coefficient close to 1. And indeed…

> reg=glm(N~log(E),data=df,family=poisson)
> summary(reg)

Call:
glm(formula = N ~ log(E), family = poisson, data = df)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.0810  -0.8373  -0.1493   0.5676   3.9001  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.03350    0.08546  -12.09   <2e-16 ***
log(E)       1.00920    0.03292   30.66   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 2553.6  on 982  degrees of freedom
Residual deviance: 1064.2  on 981  degrees of freedom
AIC: 3762.7

Number of Fisher Scoring iterations: 5

If we keep the offset, and add the variable, we can see that it become useless (which is a test of a unit parameter, somehow)

> reg=glm(N~log(E)+offset(log(E)),data=df,family=poisson)
> summary(reg)

Call:
glm(formula = N ~ log(E) + offset(log(E)), family = poisson, 
    data = df)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.0810  -0.8373  -0.1493   0.5676   3.9001  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.033503   0.085460 -12.093   <2e-16 ***
log(E)       0.009201   0.032920   0.279     0.78    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 1064.3  on 982  degrees of freedom
Residual deviance: 1064.2  on 981  degrees of freedom
AIC: 3762.7

Number of Fisher Scoring iterations: 5

Here, we do have pure Poisson processes, so exposure is crucial, since the parameter of the Poisson distribution is proportional to the exposure. But we cannot learn anything else from the exposure.

Consider some real data.

> head(baseFREQ)
  nocontrat exposition zone puissance agevehicule
1        27       0.87    C         7           0
2       115       0.72    D         5           0
3       121       0.05    C         6           0
4       142       0.90    C        10          10
5       155       0.12    C         7           0
6       186       0.83    C         5           0
  ageconducteur bonus marque carburant densite region nbre
1            56    50     12         D      93     13    0
2            45    50     12         E      54     13    0
3            37    55     12         D      11     13    0
4            42    50     12         D      93     13    0
5            59    50     12         E      73     13    0
6            75    50     12         E      42     13    0

What do we get if we consider a Poisson regression on the logarithm of the exposure ?

> reg=glm(nbre~log(exposition),data=baseFREQ,family=poisson)
> summary(reg)

Call:
glm(formula = nbre ~ log(exposition), family = poisson, data = baseFREQ)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.3988  -0.3388  -0.2786  -0.1981  12.9036  

Coefficients:
                Estimate Std. Error z value Pr(>|z|)    
(Intercept)     -2.83045    0.02822 -100.31   <2e-16 ***
log(exposition)  0.53950    0.02905   18.57   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 12931  on 49999  degrees of freedom
Residual deviance: 12475  on 49998  degrees of freedom
AIC: 16150

Number of Fisher Scoring iterations: 6

If we add the exposure to the offset, what’s happening ? (let us use a nonparametric transformation, so visualize what’s going on)

> library(gam)
> reg=gam(nbre~offset(log(exposition))+s(exposition),data=baseFREQ,family=poisson)
> plot(reg,se=TRUE)

There is a clear and significant effect. The more insured stay, the less likely they get a claim. Actually, it can be observed without running a regression.

> i1=which(baseFREQ$nbre>0)
> i0=which(baseFREQ$nbre==0)
> h1=hist(baseFREQ$exposition[i1],probability=TRUE)
> h0=hist(baseFREQ$exposition[i0],probability=TRUE)
> plot(h1$mids,h1$density,type='s',lwd=2,col="red")
> lines(h0$mids,h0$density,type='s',col='blue',lwd=2)

In blue, we have the density of the exposure for those who did not have claims, and in red, the density of those who did have one claim (or more)

So here, we cannot assume a unit value for the parameter. What does that mean ? Can we reproduce such a behavior ?

In order to get a better understandung, consider two possible behaviors for the insured. The first one will be the following : if the company does not offer substantial discounts after no several years with no claims, the insured might leave the company. For instance, if the insured has no claim during 5 years, then after 5 years, he will leave the company (to get a better price somewhere else, say). The code will be

> for(i in 1:n){
+   expo=D2-arrival[i]
+   w=c(0,0)
+   while((max(w)<expo) & (max(diff(w))<1500)) w=c(w,max(w)+trunc(rexp(1,1/1000)))
+   if(max(diff(w))>1500) departure[i]=arrival[i]+max(w[-length(w)])+1500
+   exposure[i]=departure[i]-arrival[i]
+   N[i]=max(0,length(w)-3)}
> df=data.frame(N=N,E=exposure/365)

Here, I consider 1500 days, instead of 5 years,, but it is the same idea. So, what do we have here ?

> reg=glm(N~log(E),data=df,family=poisson)
> summary(reg)

Call:
glm(formula = N ~ log(E), family = poisson, data = df)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.5684  -0.9668  -0.2321   0.4244   3.6265  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -2.50844    0.10286  -24.39   <2e-16 ***
log(E)       1.65738    0.04494   36.88   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 2567.31  on 982  degrees of freedom
Residual deviance:  885.71  on 981  degrees of freedom
AIC: 2897.9

Here, the coefficient is (significantly) larger than 1. More precisely,

> reg=glm(N~log(E)+offset(log(E)),data=df,family=poisson)
> summary(reg)

Call:
glm(formula = N ~ log(E) + offset(log(E)), family = poisson, 
    data = df)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.5684  -0.9668  -0.2321   0.4244   3.6265  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -2.50844    0.10286  -24.39   <2e-16 ***
log(E)       0.65738    0.04494   14.63   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 1114.24  on 982  degrees of freedom
Residual deviance:  885.71  on 981  degrees of freedom
AIC: 2897.9

There is clearly a bias here : people staying long are more like likely to have an accident. Which is consistent with our story, since clients with low risks left.

The second behavior will be the following : sometimes, the insured are not satisfied with the way claims are handled, and they might leave after the first claim. Consider the case where, after one claim, it is likely (e.g. with probability 50%) that the insured leaves the company. Instead of assuming that the insured did not like claims management, consider the case were the car is so damaged that he cannot drive it anymore. So it will be useless to pay an insurance premium. The code here will be

> for(i in 1:n){
+   expo=D2-arrival[i]
+   w=0
+   stay=TRUE
+   while((max(w)<expo) & (stay==TRUE)) { w=c(w,max(w)+trunc(rexp(1,1/1000)))
+   stay=sample(c(TRUE,FALSE),prob=c(.5,.5),size=1)}
+   N[i]=length(w)-2
+   if(stay==FALSE) {departure[i]=arrival[i]+max(w)
+   N[i]=length(w)-1}
+   exposure[i]=departure[i]-arrival[i]}
> df=data.frame(N=N,E=exposure/365)

Here, after each claim, the insured toss a coin to see if he cancels the contract, or not.

> reg=glm(N~log(E),data=df,family=poisson)
> summary(reg)

Call:
glm(formula = N ~ log(E), family = poisson, data = df)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-2.28402  -0.47763  -0.08215   0.33819   2.37628  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  0.09920    0.04251   2.334   0.0196 *  
log(E)       0.30640    0.02511  12.203   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 666.92  on 982  degrees of freedom
Residual deviance: 498.29  on 981  degrees of freedom
AIC: 2666.3

This time, the parameter is (again significantly) smaller than one.

> reg=glm(N~log(E)+offset(log(E)),data=df,family=poisson)
> summary(reg)

Call:
glm(formula = N ~ log(E) + offset(log(E)), family = poisson, 
    data = df)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-2.28402  -0.47763  -0.08215   0.33819   2.37628  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  0.09920    0.04251   2.334   0.0196 *  
log(E)      -0.69360    0.02511 -27.625   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 1116.87  on 982  degrees of freedom
Residual deviance:  498.29  on 981  degrees of freedom
AIC: 2666.3

The story is now rather different, since those who stay long should not have encountered a lot of opportunities to leave. So clearly, they did not have much claims. If someone has a long exposure, the negative sign in the output above means that he should not have much claims, on average.

As we can see, those models produce rather difference outputs. Note that it is possible much more interpretations. For instance, depending on the way data were extracted,

  • all policies observed, over those twenty years,
  • all policies in force at some specific date, until now
  • all policies in force at some specific date, until one year after
  • all policies in force now

So far, we have been using the first method, but the other ones will yield different interpretations, e.g. because of survivor bias. But that’s another story… And one can read Boucher and Denuit (2008) to go further.

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]]
+   vm[i]=meani=weighted.mean(Yi/Ei,Ei)    # moyenne 
+   vv[i]=variancei=sum((Yi-meani*Ei)^2)/sum(Ei)    # variance
+  cat("Class ",levels(X)[i],"average =",meani," variance =",variancei,"\n")
+ }
Class D A (17,24]  average = 0.06274415  variance = 0.06174966 
Class D A (24,40]  average = 0.07271905  variance = 0.07675049 
Class D A (40,65]  average = 0.05432262  variance = 0.06556844 
Class D A (65,101] average = 0.03026999  variance = 0.02960885 
Class D B (17,24]  average = 0.2383109   variance = 0.2442396 
Class D B (24,40]  average = 0.06662015  variance = 0.07121064 
Class D B (40,65]  average = 0.05551854  variance = 0.05543831 
Class D B (65,101] average = 0.0556386   variance = 0.0540786 
Class D C (17,24]  average = 0.1524552   variance = 0.1592623 
Class D C (24,40]  average = 0.0795852   variance = 0.09091435 
Class D C (40,65]  average = 0.07554481  variance = 0.08263404 
Class D C (65,101] average = 0.06936605  variance = 0.06684982 
Class D D (17,24]  average = 0.1584052   variance = 0.1552583 
Class D D (24,40]  average = 0.1079038   variance = 0.121747 
Class D D (40,65]  average = 0.06989518  variance = 0.07780811 
Class D D (65,101] average = 0.0470501   variance = 0.04575461 
Class D E (17,24]  average = 0.2007164   variance = 0.2647663 
Class D E (24,40]  average = 0.1121569   variance = 0.1172205 
Class D E (40,65]  average = 0.106563    variance = 0.1068348 
Class D E (65,101] average = 0.1572701   variance = 0.2126338 
Class D F (17,24]  average = 0.2314815   variance = 0.1616788 
Class D F (24,40]  average = 0.1690485   variance = 0.1443094 
Class D F (40,65]  average = 0.08496827  variance = 0.07914423 
Class D F (65,101] average = 0.1547769   variance = 0.1442915 
Class E A (17,24]  average = 0.1275345   variance = 0.1171678 
Class E A (24,40]  average = 0.04523504  variance = 0.04741449 
Class E A (40,65]  average = 0.05402834  variance = 0.05427582 
Class E A (65,101] average = 0.04176129  variance = 0.04539265 
Class E B (17,24]  average = 0.1114712   variance = 0.1059153 
Class E B (24,40]  average = 0.04211314  variance = 0.04068724 
Class E B (40,65]  average = 0.04987117  variance = 0.05096601 
Class E B (65,101] average = 0.03123003  variance = 0.03041192 
Class E C (17,24]  average = 0.1256302   variance = 0.1310862 
Class E C (24,40]  average = 0.05118006  variance = 0.05122782 
Class E C (40,65]  average = 0.05394576  variance = 0.05594004 
Class E C (65,101] average = 0.04570239  variance = 0.04422991 
Class E D (17,24]  average = 0.1777142   variance = 0.1917696 
Class E D (24,40]  average = 0.06293331  variance = 0.06738658 
Class E D (40,65]  average = 0.08532688  variance = 0.2378571 
Class E D (65,101] average = 0.05442916  variance = 0.05724951 
Class E E (17,24]  average = 0.1826558   variance = 0.2085505 
Class E E (24,40]  average = 0.07804062  variance = 0.09637156 
Class E E (40,65]  average = 0.08191469  variance = 0.08791804 
Class E E (65,101] average = 0.1017367   variance = 0.1141004 
Class E F (17,24]  average = 0           variance = 0 
Class E F (24,40]  average = 0.07731177  variance = 0.07415932 
Class E F (40,65]  average = 0.1081142   variance = 0.1074324 
Class E F (65,101] average = 0.09071118  variance = 0.1170159

Again, one can plot the variance against the average,

> plot(vm,vv,cex=sqrt(ve),col="grey",pch=19,
+ xlab="Empirical average",ylab="Empirical variance")
> points(vm,vv,cex=sqrt(ve))
> abline(a=0,b=1,lty=2)

https://f.hypotheses.org/wp-content/blogs.dir/253/files/2013/02/Capture-d%E2%80%99e%CC%81cran-2013-02-13-a%CC%80-13.58.26.png

An alternative is to use a tree. The tree can be obtained from another variable (the insured had, or had not, a claim, during the period considered) but it should be rather close to the one we would like to model (the number of claims over the period considered). Here, I did use the whole database (with more that 600,000 lines)

> library(tree)
> T=tree((nombre>0)~as.factor(zone)+as.factor(puissance)+
+ as.factor(marque)+as.factor(carburant)+as.factor(region)+
+ agevehicule+ageconducteur,data=baseFREQ,
+ split =  "gini",minsize =25000)

The tree is the following

> plot(T)
> text(T)

https://f.hypotheses.org/wp-content/blogs.dir/253/files/2013/02/Capture-d%E2%80%99e%CC%81cran-2013-02-13-a%CC%80-13.55.13.png

Now, each knot defines a class, and it is possible to use it to define a class. Which is supposed to be homogeneous.

> X=as.factor(T$where)
> E=sinistres$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]]
+   vm[i]=meani=weighted.mean(Yi/Ei,Ei)    # moyenne 
+   vv[i]=variancei=sum((Yi-meani*Ei)^2)/sum(Ei)    # variance
+  cat("Class ",levels(X)[i],"average =",meani," variance =",variancei,"\n")
+  }
Class  6 average =   0.04010406  variance = 0.04424163 
Class  8 average =   0.05191127  variance = 0.05948133 
Class  9 average =   0.07442635  variance = 0.08694552 
Class  10 average =  0.4143646   variance = 0.4494002 
Class  11 average =  0.1917445   variance = 0.1744355 
Class  15 average =  0.04754595  variance = 0.05389675 
Class  20 average =  0.08129577  variance = 0.0906322 
Class  22 average =  0.05813419  variance = 0.07089811 
Class  23 average =  0.06123807  variance = 0.07010473 
Class  24 average =  0.06707301  variance = 0.07270995 
Class  25 average =  0.3164557   variance = 0.2026906 
Class  26 average =  0.08705041  variance = 0.108456 
Class  27 average =  0.06705214  variance = 0.07174673 
Class  30 average =  0.05292652  variance = 0.06127301 
Class  31 average =  0.07195285  variance = 0.08620593 
Class  32 average =  0.08133722  variance = 0.08960552 
Class  34 average =  0.1831559   variance = 0.2010849 
Class  39 average =  0.06173885  variance = 0.06573939 
Class  41 average =  0.07089419  variance = 0.07102932 
Class  44 average =  0.09426152  variance = 0.1032255 
Class  47 average =  0.03641669  variance = 0.03869702 
Class  49 average =  0.0506601   variance = 0.05089276 
Class  50 average =  0.06373107  variance = 0.06536792 
Class  51 average =  0.06762947  variance = 0.06926191 
Class  56 average =  0.06771764  variance = 0.07122379 
Class  57 average =  0.04949142  variance = 0.05086885 
Class  58 average =  0.2459016   variance = 0.2451116 
Class  59 average =  0.05996851  variance = 0.0615773 
Class  61 average =  0.07458053  variance = 0.0818608 
Class  63 average =  0.06203737  variance = 0.06249892 
Class  64 average =  0.07321618  variance = 0.07603106 
Class  66 average =  0.07332127  variance = 0.07262425 
Class  68 average =  0.07478147  variance = 0.07884597 
Class  70 average =  0.06566728  variance = 0.06749411 
Class  71 average =  0.09159605  variance = 0.09434413 
Class  75 average =  0.03228927  variance = 0.03403198 
Class  76 average =  0.04630848  variance = 0.04861813 
Class  78 average =  0.05342351  variance = 0.05626653 
Class  79 average =  0.05778622  variance = 0.05987139 
Class  80 average =  0.0374993   variance = 0.0385351 
Class  83 average =  0.06721729  variance = 0.07295168 
Class  86 average =  0.09888492  variance = 0.1131409 
Class  87 average =  0.1019186   variance = 0.2051122 
Class  88 average =  0.05281703  variance = 0.0635244 
Class  91 average =  0.08332136  variance = 0.09067632 
Class  96 average =  0.07682093  variance = 0.08144446 
Class  97 average =  0.0792268   variance = 0.08092019 
Class  99 average =  0.1019089   variance = 0.1072126 
Class  100 average = 0.1018262   variance = 0.1081117 
Class  101 average = 0.1106647   variance = 0.1151819 
Class  103 average = 0.08147644  variance = 0.08411685 
Class  104 average = 0.06456508  variance = 0.06801061 
Class  107 average = 0.1197225   variance = 0.1250056 
Class  108 average = 0.0924619   variance = 0.09845582 
Class  109 average = 0.1198932   variance = 0.1209162

Here, when ploting the empirical variance (per knot) against the empirial average of claims, we get

https://f.hypotheses.org/wp-content/blogs.dir/253/files/2013/02/Capture-d%E2%80%99e%CC%81cran-2013-02-13-a%CC%80-14.05.08.png

Here, we can identify classes where remaining heterogeneity.

Exposure with binomial responses

Last week, we’ve seen how to take into account the exposure to compute nonparametric estimators of several quantities (empirical means, and empirical variances) incorporating exposure. Let us see what can be done if we want to model a binomial response. The model here is the following: ,

  • the number of claims https://latex.codecogs.com/gif.latex?N_i on the period https://latex.codecogs.com/gif.latex?[0,1] is unobserved
  • the number of claims https://latex.codecogs.com/gif.latex?Y_i on https://latex.codecogs.com/gif.latex?[0,E_i] is observed (as well as https://latex.codecogs.com/gif.latex?E_i)

that can be visualize below

http://f.hypotheses.org/wp-content/blogs.dir/253/files/2013/02/Capture-d%E2%80%99e%CC%81cran-2013-02-01-a%CC%80-09.30.00.png

Consider the case where the variable of interest is not the number of claims, but simply the indicator of the occurrence of a claim. Then we wish to model the event https://latex.codecogs.com/gif.latex?\{N=0\} versus https://latex.codecogs.com/gif.latex?\{N%3E0\}, interpreted as non-occurrence and occurrence. Given the fact that we can only observe https://latex.codecogs.com/gif.latex?\{Y=0\} versus https://latex.codecogs.com/gif.latex?\{Y%3E0\}. Having an inclusion is not enough to derive a model. Actually, with a Poisson process model, we can get easily that

https://latex.codecogs.com/gif.latex?\mathbb{P}(Y=0)%20=%20\mathbb{P}(N=0)^E

With words, it means that the probability of not having a claim in the first six months of the year is the square root of not have a claim over a year. Which makes sense. Assume that the probability of not having a claim can be explained by some covariates, denoted https://latex.codecogs.com/gif.latex?\boldsymbol{X}, through some link function (using the GLM terminology),

https://latex.codecogs.com/gif.latex?\mathbb{P}(N=0|\boldsymbol{X})=h(\boldsymbol{X}^{\text{\sffamily%20T}}\boldsymbol{\beta})

Now, since we do observe https://latex.codecogs.com/gif.latex?Y – and not https://latex.codecogs.com/gif.latex?N – we have

https://latex.codecogs.com/gif.latex?\mathbb{P}(Y=0|\boldsymbol{X},E)=h(\boldsymbol{X}^{\text{\sffamily%20T}}\boldsymbol{\beta})^E

The dataset we will use is always the same

> 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=";")
> T=table(sinistres$nocontrat)
> T1=as.numeric(names(T))
> T2=as.numeric(T)
> nombre1 = data.frame(nocontrat=T1,nbre=T2)
> I = contrat$nocontrat%in%T1
> T1= contrat$nocontrat[I==FALSE]
> nombre2 = data.frame(nocontrat=T1,nbre=0)
> nombre=rbind(nombre1,nombre2)
> sinistres = merge(contrat,nombre)
> sinistres$nonsin = (sinistres$nbre==0)

The first model we can consider is based on the standard logistic approach, i.e.

https://latex.codecogs.com/gif.latex?\mathbb{P}(Y=0|\boldsymbol{X},E)=\left(\frac{\exp(\boldsymbol{X}^{\text{\sffamily%20T}}\boldsymbol{\beta})}{1+\exp(\boldsymbol{X}^{\text{\sffamily%20T}}\boldsymbol{\beta})}\right)^E

That’s nice, but difficult to handle with standard functions. Nevertheless, it is always possible to compute numerically the maximum likelihood estimator of https://latex.codecogs.com/gif.latex?\boldymbol{\beta} given https://latex.codecogs.com/gif.latex?(Y_i,\boldsymbol{X}_i,E_i).

> Y=sinistres$nonsin
> X=cbind(1,sinistres$ageconducteur)
> E=sinistres$exposition
> logL = function(beta){
+ 	pi=(exp(X%*%beta)/(1+exp(X%*%beta)))^E
+ 	-sum(log(dbinom(Y,size=1,prob=pi)))
+ }
> optim(fn=logL,par=c(-0.0001,-.001),
+ method="BFGS")
$par
[1] 2.14420560 0.01040707
$value
[1] 7604.073
$counts
function gradient 
      42       10 
$convergence
[1] 0
$message
NULL
> parametres=optim(fn=logL,par=c(-0.0001,-.001),
+ method="BFGS")$par

Now, let us look at alternatives, based on standard regression models. For instance a binomial-log model. Because the exposure appears as a power of the annual probability, everything would be fine if https://latex.codecogs.com/gif.latex?h was the exponential function (or https://latex.codecogs.com/gif.latex?h^{-1} was the log link function), since

https://latex.codecogs.com/gif.latex?\mathbb{P}(Y=0|\boldsymbol{X},E)=\exp(E+\boldsymbol{X}^{\text{\sffamily%20T}}\boldsymbol{\beta})

Now, if we try to code it, it starts quickly to be problematic,

> reg=glm(nonsin~ageconducteur+offset(exposition),
+ data=sinistresI,family=binomial(link="log")) 
Error: no valid set of coefficients has been found: please supply starting values

I tried (almost) everything I could, but I could not get rid of that error message,

> startglm=c(0,-.001)
> names(startglm)=c("(Intercept)","ageconducteur")
> etaglm=rep(-.01,nrow(sinistresI))
> etaglm[sinistresI$nonsin==0]=-10
> muglm=exp(etaglm)
> reg=glm(nonsin~ageconducteur+offset(exposition),
+ data=sinistresI,family=binomial(link="log"),
+ control = glm.control(epsilon=1e-5,trace=TRUE,maxit=50),
+ start=startglm,
+ etastart=etaglm,mustart=muglm)
Deviance = NaN Iterations - 1 
Error: no valid set of coefficients has been found: please supply starting values

So I decided to give up. Almost. Actually, the problem comes from the fact that https://latex.codecogs.com/gif.latex?\mathbb{P}(Y=0) is closed to 1. I guess everything would be nicer if we could work with probability close to 0. Which is possible, since

https://latex.codecogs.com/gif.latex?\mathbb{P}(Y%3E0)=1-\mathbb{P}(Y=0)%20=%201-[1-\mathbb{P}(N%3E0)]^E

where https://latex.codecogs.com/gif.latex?\mathbb{P}(N%3E0) is close to 0. So we can use Taylor’s expansion,

https://latex.codecogs.com/gif.latex?\mathbb{P}(Y%3E0)\sim1-1+E\cdot%20\mathbb{P}(N%3E0)]=E\cdot%20\mathbb{P}(N%3E0)]

Here, the exposure does no longer appears as a power of the probability, but appears multiplicatively. Of course, there are higher order terms. But let us forget them (so far). If – one more time – we consider a log link function, then we can incorporate the exposure, or to be more specific, the logarithm of the exposure.

> regopp=glm((1-nonsin)~ageconducteur+offset(log(exposition)),
+ data=sinistresI,family=binomial(link="log"))

which now works perfectly.

Now, to see a final model, perhaps we should get back to our Poisson regression model since we do have a model for the probability that https://latex.codecogs.com/gif.latex?\mathbb{P}(Y=\cdot).

> regpois=glm(nbre~ageconducteur+offset(log(exposition)),
+ data=sinistres,family=poisson(link="log"))

We can now compare those three models. Perhaps, we should also include the prediction without any explanatory variable. For the second model (actually, it does run without any explanatory variable), we run

>  regreff=glm((1-nonsin)~1+offset(log(exposition)),
+ data=sinistres,family=binomial(link="log"))

so that the prediction is here

> exp(coefficients(regreff))
(Intercept) 
 0.06776376

This value is comparable with the logistic regression,

> logL2 = function(beta){
+ 	pi=(exp(beta)/(1+exp(beta)))^E
+ 	-sum(log(dbinom(Y,size=1,prob=pi)))}
> param=optim(fn=logL2,par=.01,method="BFGS")$par
> 1-exp(param)/(1+exp(param))
[1] 0.06747777

But is quite different from the Poisson model,

> exp(coefficients(glm(nbre~1+offset(log(exposition)),
+ data=sinistres,family=poisson(link="log"))))
(Intercept) 
 0.07279295

Let us produce a graph, to compare those models,

> age=18:100
> yml1=exp(parametres[1]+parametres[2]*age)/(1+exp(parametres[1]+parametres[2]*age))
> plot(age,1-yml1,type="l",col="purple")
> yp=predict(regpois,newdata=data.frame(ageconducteur=age,
+ exposition=1),type="response")
> yp1=1-exp(-yp)
> ydl=predict(regopp,newdata=data.frame(ageconducteur=age,
+ exposition=1),type="response")
> plot(age,ydl,type="l",col="red")
> lines(age,yp1,type="l",col="blue")
> lines(age,1-yml1,type="l",col="purple")
> abline(h=exp(coefficients(regreff)),lty=2)

https://f.hypotheses.org/wp-content/blogs.dir/253/files/2013/02/Capture-d%E2%80%99e%CC%81cran-2013-02-08-a%CC%80-14.55.591.png

Observe here that the three models are quite different. Actually, with two models, it is possible to run more complex regression, e.g. with splines, to visualize the impact of the age on the probability of having – or not – a car accident. If we compare the Poisson regression (still in red) and the log-binomial model, with Taylor’s expansion, we get

https://f.hypotheses.org/wp-content/blogs.dir/253/files/2013/02/Capture-d%E2%80%99e%CC%81cran-2013-02-08-a%CC%80-14.39.08.png

The next step is to see how to incorporate the exposure in a tree. But that’s another story…

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 been here for a few months, or weeks). Somehow, simple results because more complicated to compute just because we have to take into account the fact that exposure is an heterogeneous variable.

The exposure in insurance ratemaking can be seen as a problem of censored data (in my dataset, the exposure is always smaller than 1 since observations are contracts, not policyholders),

  • the number of claims https://latex.codecogs.com/gif.latex?N_i on the period https://latex.codecogs.com/gif.latex?[0,1] is unobserved
  • the number of claims https://latex.codecogs.com/gif.latex?Y_i on https://latex.codecogs.com/gif.latex?[0,E_i] is observed (as well as https://latex.codecogs.com/gif.latex?E_i)

And as always, the variable of interest is the unobserved one, because we have to price insurance contract with a cover period of one (full) year. So we have to model the yearly frequency of insurance claims.

https://f.hypotheses.org/wp-content/blogs.dir/253/files/2013/02/Capture-d%E2%80%99e%CC%81cran-2013-02-01-a%CC%80-09.30.00.png

In our dataset, we have https://latex.codecogs.com/gif.latex?(Y_i,E_i)‘s – or more generally also some additional covariates https://latex.codecogs.com/gif.latex?(Y_i,E_i,\boldsymbol{X}_i)‘s. For ratemaking, we need to estimate https://latex.codecogs.com/gif.latex?\mathbb{E}(N\vert\boldsymbol{X}=\boldsymbol{x}) and perhaps also https://latex.codecogs.com/gif.latex?\text{Var}(N|\boldsymbol{X}=\boldsymbol{x}) (for instance to test if the Poisson assumption is valid, or not). To estimate the expected value, a natural estimate for https://latex.codecogs.com/gif.latex?\mathbb{E}(N) (forget about covariates as a start) is
https://latex.codecogs.com/gif.latex?m_N=\frac{\sum_{i=1}^n%20Y_i}{\sum_{i=1}^n%20E_i}
which is also the weight average of annualized individual counts
https://latex.codecogs.com/gif.latex?m_N=\sum_{i=1}^n%20\frac{%20E_i}{\sum_{i=1}^n%20E_i}%20\cdot%20\frac{Y_i}{E_i}
We consider the ratio of the total number of claims to the total exposure-to-
risk. This estimate appears for instance if we consider a Poisson process, so that https://latex.codecogs.com/gif.latex?N\sim\mathcal{P}(\lambda) while https://latex.codecogs.com/gif.latex?Y\sim\mathcal{P}(\lambda%20\cdot%20E). Then, the likelihood is

https://latex.codecogs.com/gif.latex?\mathcal{L}(\lambda,\boldsymbol{Y},\boldsymbol{E})=\prod_{i=1}^n%20\frac{e^{-\lambda%20E_i}%20[\lambda%20E_i]^{Y_i}}{Y_i!}

i.e.

https://latex.codecogs.com/gif.latex?\log%20\mathcal{L}(\lambda,\boldsymbol{Y},\boldsymbol{E})%20=%20-\lambda%20\sum_{i=1}^n%20E_i%20+\sum_{i=1}^n%20Y_i%20\log[\lambda%20E_i]%20-%20\log\left(\prod_{i=1}^n%20Y_i!\right)

The first order condition is here

https://latex.codecogs.com/gif.latex?\frac{\partial}{\partial%20\lambda}\log%20\mathcal{L}(\lambda,\boldsymbol{Y},\boldsymbol{E})%20=%20%20-%20\sum_{i=1}^n%20E_i%20+\frac{1}{\lambda}\sum_{i=1}^n%20Y_i%20=0

which is satisfied if

https://latex.codecogs.com/gif.latex?\widehat{\lambda}=\frac{\sum_{i=1}^n%20Y_i}{\sum_{i=1}^n%20E_i}

So, we do have an estimator for the expected value, and a natural estimator for https://latex.codecogs.com/gif.latex?\mathbb{E}(N\vert\boldsymbol{X}=\boldsymbol{x}) is then (if we consider categorical covariates)
https://latex.codecogs.com/gif.latex?m_{N|\boldsymbol{x}}%20=\frac{\sum_{i,\boldsymbol{X}_i=\boldsymbol{x}}%20Y_i}{\sum_%20{i,\boldsymbol{X}_i=\boldsymbol{x}}%20E_i}

Now, we need an estimate for the variance, or more precisely the conditional variable. Assume (as a starting point) that all have the same exposure https://latex.codecogs.com/gif.latex?E. For instance, if https://latex.codecogs.com/gif.latex?E is one half, insured were observed only the first six months. Then https://latex.codecogs.com/gif.latex?N=Y+Y%27 with https://latex.codecogs.com/gif.latex?Y\overset{\mathcal%20L}{=}Y%27 (https://latex.codecogs.com/gif.latex?Y is the number of claims on the first six months, while https://latex.codecogs.com/gif.latex?Y%27 are the number of claims on the last six months), i.e. https://latex.codecogs.com/gif.latex?\text{Var}(N)=\text{Var}(Y)+%20\text{Var}(Y%27) if we assume independent increments. I.e.
https://latex.codecogs.com/gif.latex?\text{Var}(N)=2\text{Var}(Y), or conversely https://latex.codecogs.com/gif.latex?E%20\cdot\text{Var}(N)=\text{Var}(Y). More generally, it is reasonable to assume that

https://latex.codecogs.com/gif.latex?\text{Var}(Y)=E\cdot%20\text{Var}(N)
for all values of https://latex.codecogs.com/gif.latex?E. And then
https://latex.codecogs.com/gif.latex?\text{Var}\left(\frac{Y}{E}\right)=\frac{1}{E}\cdot%20\text{Var}(N)
Thus, it seems legitimate to assume that the empirical variance of https://latex.codecogs.com/gif.latex?N can be written
https://latex.codecogs.com/gif.latex?S_N^2=E\cdot%20S_{Y/E}^2
Since the average of https://latex.codecogs.com/gif.latex?Y_i/E is https://latex.codecogs.com/gif.latex?\overline{N}=m_N, then
https://latex.codecogs.com/gif.latex?S_N^2=E\cdot%20\frac{1}{n}\sum_{i=1}^n%20\left[\frac{Y_i}{E}-\overline{N}\right]^2}%20=%20\frac{1}{n}\sum_{i=1}^n%20E\left[\frac{Y_i}{E}-\overline{N}\right]^2}
or equivalently
https://latex.codecogs.com/gif.latex?S_N^2=\frac{1}{n}\sum_{i=1}^n%20\frac{E}{E^2}\left[Y_i-\overline{N}\cdot%20E\right]^2}%20=\frac{1}{n}\sum_{i=1}^n%20\frac{1}{E}[Y_i-\overline{N}\cdot%20E]^2i.e.
https://latex.codecogs.com/gif.latex?S_N^2=\frac{\sum_{i=1}^n%20[Y_i-\overline{N}\cdot%20E]^2%20}{nE}
Thus, with different https://latex.codecogs.com/gif.latex?E_i‘s, it would be legitimate (I guess) to consider
https://latex.codecogs.com/gif.latex?S_N^2=\frac{\sum_{i=1}^n%20[Y_i-\overline{N}\cdot%20E_i]^2%20}{\sum_{i=1}^n%20E_i}
Thus, an estimator for https://latex.codecogs.com/gif.latex?\text{Var}(N|\boldsymbol{X}=\boldsymbol{x}) is
https://latex.codecogs.com/gif.latex?S_{N|\boldsymbol{x}}^2=\frac{\sum_{i,\boldsymbol{X}_i=\boldsymbol{x}}%20[Y_i-\overline{N}\cdot%20E_i]^2}{\sum_{i,\boldsymbol{X}_i=\boldsymbol{x}%20}%20E_i}

This can be used to test is the Poisson assumption is valid to model frequency. Consider the following dataset,

>  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=";")
>  T=table(sinistres$nocontrat)
>  T1=as.numeric(names(T))
>  T2=as.numeric(T)
>  nombre1 = data.frame(nocontrat=T1,nbre=T2)
>  I = contrat$nocontrat%in%T1
>  T1= contrat$nocontrat[I==FALSE]
>  nombre2 = data.frame(nocontrat=T1,nbre=0)
>  nombre=rbind(nombre1,nombre2)
>  baseFREQ = merge(contrat,nombre)

Here, we do have our two variables of interest, the exposure, per contract,

>  E <- baseFREQ$exposition

and the (observed) number of claims (during that time frame)

>  Y <- baseFREQ$nbre

It is possible to compute without covariates, the average (yearly) number of claims, per contract, and the associated variance

> (mean=weighted.mean(Y/E,E))
[1] 0.07279295
> (variance=sum((Y-mean*E)^2)/sum(E)) 
[1] 0.08778567

It looks like the variance is (slightly) larger than the average (we’ll see in a few weeks how to test it, more formally). It is possible to add covariates, for instance the density of population, in the area where the policyholder lives,

>  X=as.factor(baseFREQ$densite)
>  for(i in 1:length(levels(X))){
+ 	   Ei=E[X==levels(X)[i]]
+ 	   Yi=Y[X==levels(X)[i]]
+  (meani=weighted.mean(Yi/Ei,Ei))    # moyenne 
+  (variancei=sum((Yi-meani*Ei)^2)/sum(Ei))    # variance
+ cat("Density, zone",levels(X)[i],"average =",meani," variance =",variancei,"\n")
+ }
Density, zone 11 average = 0.07962411  variance = 0.08711477 
Density, zone 21 average = 0.05294927  variance = 0.07378567 
Density, zone 22 average = 0.09330982  variance = 0.09582698 
Density, zone 23 average = 0.06918033  variance = 0.07641805 
Density, zone 24 average = 0.06004009  variance = 0.06293811 
Density, zone 25 average = 0.06577788  variance = 0.06726093 
Density, zone 26 average = 0.0688496   variance = 0.07126078 
Density, zone 31 average = 0.07725273  variance = 0.09067 
Density, zone 41 average = 0.03649222  variance = 0.03914317 
Density, zone 42 average = 0.08333333  variance = 0.1004027 
Density, zone 43 average = 0.07304602  variance = 0.07209618 
Density, zone 52 average = 0.06893741  variance = 0.07178091 
Density, zone 53 average = 0.07725661  variance = 0.07811935 
Density, zone 54 average = 0.07816105  variance = 0.08947993 
Density, zone 72 average = 0.08579731  variance = 0.09693305 
Density, zone 73 average = 0.04943033  variance = 0.04835521 
Density, zone 74 average = 0.1188611   variance = 0.1221675 
Density, zone 82 average = 0.09345635  variance = 0.09917425 
Density, zone 83 average = 0.04299708  variance = 0.05259835 
Density, zone 91 average = 0.07468126  variance = 0.3045718 
Density, zone 93 average = 0.08197912  variance = 0.09350102 
Density, zone 94 average = 0.03140971  variance = 0.04672329

Perhaps graphs would be a nice tool to play with, to visualize that information

> plot(meani,variancei,cex=sqrt(Ei),col="grey",pch=19,
+ xlab="Empirical average",ylab="Empirical variance")
> points(meani,variancei,cex=sqrt(Ei))

https://f.hypotheses.org/wp-content/blogs.dir/253/files/2013/02/Capture-d%E2%80%99e%CC%81cran-2013-02-01-a%CC%80-10.51.26.png

The size of the circles is related to the size of the group (the area is proportional to the total exposure within the group). The first diagonal corresponds to the Poisson model, i.e. the variance should be equal to the mean. It is also possible to consider other covariates, like the gas type

https://f.hypotheses.org/wp-content/blogs.dir/253/files/2013/02/Capture-d%E2%80%99e%CC%81cran-2013-02-01-a%CC%80-10.52.02.png

or the car brand,

https://f.hypotheses.org/wp-content/blogs.dir/253/files/2013/02/Capture-d%E2%80%99e%CC%81cran-2013-02-01-a%CC%80-10.50.49.png

It is also possible to consider the age of the driver as a categorical variate

https://f.hypotheses.org/wp-content/blogs.dir/253/files/2013/02/Capture-d%E2%80%99e%CC%81cran-2013-02-01-a%CC%80-10.51.40.png

Actually, the age is interesting: we can observe on that dataset a feature that Jean-Philippe Boucher observed also on his own datasets. Let us look more carefully where are the different ages,

https://f.hypotheses.org/wp-content/blogs.dir/253/files/2013/02/Capture-d%E2%80%99e%CC%81cran-2013-02-01-a%CC%80-10.55.17.png

On the right, we can observe young (unexperienced) drivers. That was expected. But some classes are below the first diagonal: the expected frequency is large, but not the variance. I.e. we know for sure that young drivers have more car accidents. It is not an heterogeneous class, on the contrary: young drivers can be seen as a relatively homogeneous class, with a high frequency of car accidents.

With the original dataset (here, I use only a subset with 50,000 clients), we do obtain the following graph:

https://f.hypotheses.org/wp-content/blogs.dir/253/files/2013/02/Capture-d%E2%80%99e%CC%81cran-2013-02-01-a%CC%80-11.27.04.png

If we do not observe underdispersion for young drivers, observe that those are incredibly homogeneous classes. With a clear impact of experience, since circles are moving downward from age 18 to 25.

Another disturbing story (this was – one more time – suggestion from Jean-Philippe) that it might be possible to consider the exposure as a standard variable, and see if the coefficient is actually equal to 1. Without any covariate,

>  reg=glm(Y~log(E),family=poisson("log"))
>  summary(reg)

Call:
glm(formula = Y ~ log(E), family = poisson("log"))

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.3988  -0.3388  -0.2786  -0.1981  12.9036  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -2.83045    0.02822 -100.31   <2e-16 ***
log(E)       0.53950    0.02905   18.57   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 12931  on 49999  degrees of freedom
Residual deviance: 12475  on 49998  degrees of freedom
AIC: 16150

Number of Fisher Scoring iterations: 6

i.e. the parameter is clearly strictly smaller than 1. And it is neither related to significance,

> library(car)
> linearHypothesis(reg,"log(E)",1)
Linear hypothesis test

Hypothesis:
log(E) = 1

Model 1: restricted model
Model 2: Y ~ log(E)

  Res.Df Df  Chisq Pr(>Chisq)    
1  49999                         
2  49998  1 251.19  < 2.2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

nor to the fact that I did not take into account covariates,

> reg=glm(nbre~log(exposition)+carburant+as.factor(ageconducteur)+as.factor(densite),family=poisson("log"),data=baseFREQ)
>  summary(reg)

Call:
glm(formula = nbre ~ log(exposition) + carburant + as.factor(ageconducteur) + 
    as.factor(densite), family = poisson("log"), data = baseFREQ)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.7114  -0.3200  -0.2637  -0.1896  12.7104  

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                  -14.07321  181.04892  -0.078 0.938042    
log(exposition)                0.56781    0.03029  18.744  < 2e-16 ***
carburantE                    -0.17979    0.04630  -3.883 0.000103 ***
as.factor(ageconducteur)19    12.18354  181.04915   0.067 0.946348    
as.factor(ageconducteur)20    12.48752  181.04902   0.069 0.945011

(etc). So it might be a too strong assumption to assume that the exposure is an exogenous variate here. But that’s another story !