Mardi, on commencera le cours d’actuariat, avec un cours d’introduction le matin, mais l’après midi, on attaquera plus sérieusement, avec une mise en bouche, avec les modèles de classification (de type {survenance} vs {non survenance} d’accident). Là encore, les slides sont en ligne.
Je parlerais aussi du premier devoir, qui portera sur les modèles que nous (re)verrons ici.
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: ,
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
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
This Monday I will be in Lille to give a talk at the Journées de Statistiques. The talk will be based on the joint work with Gery Geenens and Davy Paindaveine, on “Probit transformation for nonparametric kernel estimation of the copula density”. The papier can be found online, on http://arxiv.org/abs/1404.4414
“Copula modelling has become ubiquitous in modern statistics. Here, the problem of nonparametrically estimating a copula density is addressed. Arguably the most popular nonparametric density estimator, the kernel estimator is not suitable for the unit-square-supported copula densities, mainly because it is heavily affected by boundary bias issues. In addition, most common copulas admit unbounded densities, and kernel methods are not consistent in that case. In this paper, a kernel-type copula density estimator is proposed. It is based on the idea of transforming the uniform marginals of the copula density into normal distributions via the probit function, estimating the density in the transformed domain, which can be accomplished without boundary problems, and obtaining an estimate of the copula density through back-transformation. Although natural, a raw application of this procedure was, however, seen not to perform very well in the earlier literature. Here, it is shown that, if combined with local likelihood density estimation methods, the idea yields very good and easy to implement estimators, fixing boundary issues in a natural way and able to cope with unbounded copula densities. The asymptotic properties of the suggested estimators are derived, and a practical way of selecting the crucially important smoothing parameters is devised. Finally, extensive simulation studies and a real data analysis evidence their excellent performance compared to their main competitors.”
The slides are available on Dropbox (it is a 54Mo file with animated pictures, that do not appear on the version below).
The Gaussian and the (log) Poisson regressions share a very interesting property,
i.e. the average predicted value is the empirical mean of our sample.
> mean(predict(lm(dist~speed,data=cars))) [1] 42.98 > mean(cars$dist) [1] 42.98
One can prove that it is also the prediction for the average individual in our sample
> predict(lm(dist~speed,data=cars), + newdata=data.frame(speed=mean(cars$speed))) 42.98
The geometric interpretation is that the regression line passes through the centroid,
> plot(cars) > abline(lm(dist~speed,data=cars),col="red") > abline(h=mean(cars$dist),col="blue") > abline(v=mean(cars$speed),col="blue") > points(mean(cars$speed),mean(cars$dist))
But in all other cases, it is no longer the case. Consider for instance the case of a logistic regression. And to ask for something even more complicated, consider the case where we have only categorical explanatory variables. In that context, it is more difficult to get a prediction for the “average individual”. Unless we consider some fuzzy interpretation of the regression.
Continue reading Classification with Categorical Variables (the fuzzy side)
Pour le cours de mercredi prochain, la base utilisée sera une base tirée du livre de Jed Frees, http://instruction.bus.wisc.edu/jfrees/…
> baseavocat=read.table("http://freakonometrics.free.fr/AutoBI.csv",header=TRUE,sep=",") > tail(baseavocat) CASENUM ATTORNEY CLMSEX MARITAL CLMINSUR SEATBELT CLMAGE LOSS 1335 34204 2 2 2 2 1 26 0.161 1336 34210 2 1 2 2 1 NA 0.576 1337 34220 1 2 1 2 1 46 3.705 1338 34223 2 2 1 2 1 39 0.099 1339 34245 1 2 2 1 1 18 3.277 1340 34253 2 2 2 2 1 30 0.688
On dispose d’une variable dichotomique indiquant si un assuré – suite à un accident de la route – a été représenté par un avocat (1 si oui, 2 si non). On connaît le sexe de l’assuré (1 pour les hommes et 2 pour les femmes), le statut marital (1 s’il est marié, 2 s’il est célibataire, 3 pour un veuf, et 4 pour un assuré divorcé). On sait aussi si l’assuré portait ou non une ceinture de sécurité lorsque l’accident s’est produit (1 si oui, 2 si non et 3 si l’information n’est pas connue). Enfin, une information pour savoir si le conducteur du véhicule était ou non assuré (1 si oui, 2 si non et 3 si l’information n’est pas connue). On va recoder un peu les données afin de les rendre plus claires à lire.
Les transparents du cours sont en ligne ici,
Sur les arbres de régression, je mettrais en ligne un billet, afin d’illustrer la méthode. En attendant des compléments théoriques peuvent se trouver en ligne http://genome.jouy.inra.fr/…, http://ensmp.fr/…, ou http://ujf-grenoble.fr/… (pour information, nous ne verrons que la méthode CART). Je peux renvoyer au livre (et au blog) de Stéphane Tuffery, ou (en anglais) au livre de Richard Berk, dont un résumé se trouve en ligne sur http://crim.upenn.edu/….
La semaine suivante, nous aborderons la régression de Poisson, les méthodes de biais minimal, et introduirons les modèles linéaires généralisés. Je renvoie au chapitre sur la tarification a priori du Denuit & Charpentier (2005), aux chapitres 12 et 13 de Frees (2010) ou encore les chapitres 5 et 6 du De Jong & Heller (2008). Pour les plus curieux qui veulent comprendre les liens entre les modèles linéaires généralisés et la tarification par crédibilité, je renvoie à l’article de Klinker (2010)
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). Pour aller un peu plus loin, quelques compléments,
pour une lecture plus économique de la problématique de la segmentation en assurance
ou pour une lecture plus légale
Sinon, plusieurs articles de vulgarisation peuvent être lu sur internet,
La première démo aura lieu lundi, en salle informatique. Karim sera une introduction au langage R, à la manipulation des variables (qualitatives et quantitatives). Je mettrais en ligne les transparents en fin de semaine, et les codes seront mis en ligne dans le courant de la semaine prochaine.
Comme annoncé hier, il n’y aura pas cours mercredi prochain. Le mercredi suivant, nous verrons la modélisation des variables indicatrices, i.e. la régression logistique, et les arbres de régression. On supposera que le modèle linéaire aura été vu, je mets un lien vers les transparents du cours ACT6420 de la session passée, notes de cours transparents1 et transparents2. Il est aussi possible de relire Frees (2010), chapitres 3, 4, 5 et 6.
Pour commencer à pratiquer la régression logistique, on utilisera la petite fonction suivante
Sinon, la Casualty Actuarial Society a mis en ligne plusieurs documents en ligne sur les arbres de régression (qui sont peu abordés dans les livres mentionnés auparavant),
pour une comparaison de toutes les méthodes
Les transparents seront mis en ligne en fin de semaine prochaine. A suivre donc…
Consider a random pair 
of binary responses, i.e. 
with 
taking values 1 or 2. Assume that probability 
can be function of some covariates.
A standard model is based a latent Gaussian structure, i.e. there exists some random vector 
such that 
if 
is lower than a given threshold, and 1 otherwise.
As in standard probit models, assume that
where we can assume that 
is a Gaussian random vector. This assumption can be used to derive the likelihood of a sample 
.
> logV=function(parameter){ + CORRELATION=parameter[1] + BETA=matrix(parameter[2:length(parameter)],ncol(Y),ncol(X)) + z=cbind(X%*%(BETA[1,]),X%*%(BETA[2,])) + sigma=matrix(c(1,CORRELATION,CORRELATION,1),2,2) + a11=pmnorm(z[1,],rep(0,ncol(Y)),varcov=sigma) + for(i in 2:nrow(z)){a11=c(a11,pmnorm(z[i,],rep(0,ncol(Y)),varcov=sigma))} + a10=pnorm(z[1,1],sd=sqrt(sigma[1,1]))-pmnorm(z[1,],varcov=sigma) + for(i in + 2:nrow(z)){a10=c(a10,pnorm(z[i,1],sd=sqrt(sigma[1,1]))-pmnorm(z[i,],varcov=sigma))} + a01=pnorm(z[1,2],sd=sqrt(sigma[2,2]))-pmnorm(z[1,],varcov=sigma) + for(i in + 2:nrow(z)){a01=c(a01,pnorm(z[i,2],sd=sqrt(sigma[2,2]))-pmnorm(z[i,],varcov=sigma))} + a00=1-a10-a01-a11 + -sum(((Y[,1]==1)&(Y[,2]==1))*log(a11) + + 1*log(a01) + + 2*log(a10) + + 3*log(a00) ) + } > OPT=optim(fn=logV,par=c(0,1,1,1,1,1,1),method="BFGS")$par
(the code is a bit long since I had trouble working properly with matrices – or more precisely to vectorize my functions – so I used loops… I am sure it is possible to write a better code).
It is possible to generate samples (based on that specific model) to check that we can actually derive proper maximum likelihood estimators,
> library(mnormt) > set.seed(1) > n=1000 > r=0.5 > X1=runif(n) > X2=rnorm(n) > Y1S=1+5*X1 > Y2S=8-5*X1 > RES=rmnorm(n,mean=c(0,0),varcov=matrix(c(1,r,r,1),2,2)) > YS=cbind(Y1S,Y2S)+RES > Y1=(YS[,1]>quantile(YS[,1],.5))*1 > Y2=(YS[,2]>quantile(YS[,2],.5))*1 > base=data.frame(i,Y1,Y2,X1,X2,YS) > head(base) i Y1 Y2 X1 X2 Y1S Y2S 1 1 0 0 0.2655087 0.07730312 3.177587 5.533884 2 2 0 0 0.3721239 -0.29686864 1.935307 5.089524 3 3 1 0 0.5728534 -1.18324224 4.757848 5.172584 4 4 1 0 0.9082078 0.01129269 4.600029 3.878225 5 5 0 1 0.2016819 0.99160104 2.547362 6.743714 6 6 1 0 0.8983897 1.59396745 5.309974 4.421523
(the two columns on the right are latent observations, that cannot be used since theoretically they are unobservable). Note that it is a simple regression, one of the component is here only to bring some noise. First of all, let us look at marginal probit regression
> reg1=glm(Y1~X1+X2,data=base,family=binomial) > reg2=glm(Y2~X1+X2,data=base,family=binomial) > summary(reg1) Call: glm(formula = Y1 ~ X1 + X2, family = binomial, data = base) Deviance Residuals: Min 1Q Median 3Q Max -2.90570 -0.50126 -0.00266 0.49162 2.78256 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -4.291725 0.267149 -16.065 <2e-16 X1 8.656836 0.510153 16.969 <2e-16 *** X2 0.007375 0.090530 0.081 0.935 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 1386.29 on 999 degrees of freedom Residual deviance: 726.48 on 997 degrees of freedom AIC: 732.48 Number of Fisher Scoring iterations: 5 > summary(reg2) Call: glm(formula = Y2 ~ X1 + X2, family = binomial, data = base) Deviance Residuals: Min 1Q Median 3Q Max -2.74682 -0.51814 -0.00001 0.57969 2.58565 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 3.91709 0.24399 16.054 <2e-16 *** X1 -7.89703 0.46277 -17.065 <2e-16 *** X2 0.18360 0.08758 2.096 0.036 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 1386.29 on 999 degrees of freedom Residual deviance: 777.61 on 997 degrees of freedom AIC: 783.61 Number of Fisher Scoring iterations: 5
Here, the optimization yields,
> OPT=optim(fn=logV,par=c(0,1,1,1,1,1,1),method="BFGS")$par > OPT[1] [1] 0.5261382 > matrix(OPT[2:7],2,3) [,1] [,2] [,3] [1,] -2.451721 4.908633 0.01600769 [2,] 2.241962 -4.539946 0.10614807
Note that the coefficients we have obtained are almost identical to the ones obtained with R standard procedure,
> library(Zelig) > REG= zelig(list(mu1=Y1~X1+X2, + mu2=Y2~X1+X2, + rho=~1), + model="bprobit",data=base) > summary(REG) Call: zelig(formula = list(mu1 = Y1 ~ X1 + X2, mu2 = Y2 ~ X1 + X2, rho = ~1), model = "bprobit", data = base) Pearson Residuals: Min 1Q Median 3Q Max probit(mu1) -10.5442 -0.377243 0.0041803 0.36709 8.60398 probit(mu2) -7.8547 -0.376888 0.0083715 0.42923 5.88264 rhobit(rho) -13.8322 -0.091502 -0.0080544 0.37218 0.85101 Coefficients: Value Std. Error t value (Intercept):1 -2.451699 0.135369 -18.11116 (Intercept):2 2.241964 0.125072 17.92536 (Intercept):3 1.169461 0.189771 6.16249 X1:1 4.908617 0.252683 19.42602 X1:2 -4.539951 0.233632 -19.43203 X2:1 0.015992 0.050443 0.31703 X2:2 0.106154 0.049092 2.16235 Number of linear predictors: 3 Names of linear predictors: probit(mu1), probit(mu2), rhobit(rho) &n bsp; Dispersion Parameter for binom2.rho family: 1 Residual Deviance: 1460.355 on 2993 degrees of freedom Log-likelihood: -730.1774 on 2993 degrees of freedom Number of Iterations: 3 > matrix(coefficients(REG)[c(1:2,4:7)],2,3) [,1] [,2] [,3] [1,] -2.451699 4.908617 0.01599183 [2,] 2.241964 -4.539951 0.10615443
The correlation here is also the same
That procedure works well an can be extended to ordinal responses (not only binary ones, or to three dimensional problems,
logV=function(beta){ BETA=matrix(beta[4:(3+ncol(Y)*ncol(X))],ncol(Y),ncol(X)) z=cbind(X%*%(BETA[1,]),X%*%(BETA[2,]),X%*%(BETA[3,])) r12=beta[1] r23=beta[2] r31=beta[3] s1=s2=s3=1 sigma=matrix(c(s1^2,r12*s1*s2,r31*s1*s3, r12*s1*s2,s2^2,r23*s2*s3, r31*s1*s3,r23*s2*s3,s3^2),3,3) sigma1=matrix(c(s2^2,r23*s2*s3, r23*s2*s3,s3^2),2,2) sigma2=matrix(c(s1^2,r31*s1*s3, r31*s1*s3,s3^2),2,2) sigma3=matrix(c(s1^2,r12*s1*s2, r12*s1*s2,s2^2),2,2) a111=pmnorm(z[1,],rep(0,ncol(Y)),varcov=sigma) for(i in 2:nrow(z)){a111=c(a111,pmnorm(z[i,],rep(0,ncol(Y)),varcov=sigma))} a011=pmnorm(z[1,2:3],varcov=sigma1)-pmnorm(z[1,],varcov=sigma) for(i in 2:nrow(z)){a011=c(a011,pmnorm(z[i,2:3],varcov=sigma1)-pmnorm(z[i,],varcov=sigma))} a101=pmnorm(z[1,c(1,3)],varcov=sigma2)-pmnorm(z[1,],varcov=sigma) for(i in 2:nrow(z)){a101=c(a101,pmnorm(z[i,c(1,3)],varcov=sigma2)-pmnorm(z[i,],varcov=sigma))} a110=pmnorm(z[1,1:2],varcov=sigma3)-pmnorm(z[1,],varcov=sigma) for(i in 2:nrow(z)){a110=c(a110,pmnorm(z[i,1:2],varcov=sigma3)-pmnorm(z[i,],varcov=sigma))} a100=pnorm(z[1,1],sd=s1)-pmnorm(z[1,c(1,2)],varcov=sigma3)-pmnorm(z[1,c(1,3)],varcov=sigma2)+pmnorm(z[1,],rep(0,ncol(Y)),varcov=sigma) for(i in 2:nrow(z)){a100=c(a100,pnorm(z[i,1],sd=s1)-pmnorm(z[i,c(1,2)],varcov=sigma3)-pmnorm(z[i,c(1,3)],varcov=sigma2)+pmnorm(z[i,],rep(0,ncol(Y)),varcov=sigma))} a010=pnorm(z[1,2],sd=s2)-pmnorm(z[1,c(1,2)],varcov=sigma3)-pmnorm(z[1,c(2,3)],varcov=sigma1)+pmnorm(z[1,],rep(0,ncol(Y)),varcov=sigma) for(i in 2:nrow(z)){a010=c(a010,pnorm(z[i,2],sd=s2)-pmnorm(z[i,c(1,2)],varcov=sigma3)-pmnorm(z[i,c(2,3)],varcov=sigma1)+pmnorm(z[i,],rep(0,ncol(Y)),varcov=sigma))} a001=pnorm(z[1,3],sd=s3)-pmnorm(z[1,c(2,3)],varcov=sigma1)-pmnorm(z[1,c(1,3)],varcov=sigma2)+pmnorm(z[1,],rep(0,ncol(Y)),varcov=sigma) for(i in 2:nrow(z)){a001=c(a001,pnorm(z[i,3],sd=s3)-pmnorm(z[i,c(2,3)],varcov=sigma1)-pmnorm(z[i,c(1,3)],varcov=sigma2)+pmnorm(z[i,],rep(0,ncol(Y)),varcov=sigma))} a000=1-a111-a011-a101-a110-a001-a010-a100 a111[a111<=0]=1e-50 a110[a110<=0]=1e-50 a101[a101<=0]=1e-50 a011[a011<=0]=1e-50 a100[a100<=0]=1e-50 a010[a010<=0]=1e-50 a001[a001<=0]=1e-50 a000[a000<=0]=1e-50 -sum(((Y[,1]==0)&(Y[,2]==0)&(Y[,3]==0))*log(a111) + 4*log(a011) + 5*log(a101) + 6*log(a110) + 7*log(a001) + 8*log(a010) + 9*log(a100) + 10*log(a000) ) }
A strong assumption in that bivariate model is that residuals have a Gaussian structure. It is possible to change that assumption
Here, the following code can be used to extend the model to non Gaussian structures,
> F=function(x,r){pmnorm(x,rep(0,length(x)), + varcov=matrix(c(1,r,r,1),2,2))} > Fx=function(x1){F(c(x1,1e40),0)} > Fy=function(x2){Fx(x2)} > > logVgen=function(parameter){ + CORRELATION=parameter[1] + BETA=matrix(parameter[2:length(parameter)],ncol(Y),ncol(X)) + z=cbind(X%*%(BETA[1,]),X%*%(BETA[2,])) + a11=F(z[1,],r=CORRELATION) + for(i in 2:nrow(z)){a11=c(a11,F(z[i,],r=CORRELATION))} + a10=Fx(z[1,1])-F(z[1,],r=CORRELATION) + for(i in 2:nrow(z)){a10=c(a10,Fx(z[i,1])-F(z[i,],r=CORRELATION))} + a01=Fy(z[1,2])-F(z[1,],r=CORRELATION) + for(i in 2:nrow(z)){a01=c(a01,Fy(z[i,2])-F(z[i,],r=CORRELATION))} + a00=1-a10-a01-a11 + -sum(((Y[,1]==1)&(Y[,2]==1))*log(a11) + + 11*log(a01) + + 12*log(a10) + + 13*log(a00) ) + } > > beta0=c(0,1,1,1,1,1,1) > (OPT=optim(fn=logVgen,par=beta0,method="BFGS")$par) [1] 0.52613820 -2.45172059 2.24196154 4.90863292 -4.53994592 0.01600769 [7] 0.10614807 There were 23 warnings (use warnings() to see them)
E.g.
> library(copula) > F=function(x,r){pcopula(pnorm(x), claytonCopula(2, r))} > Fx=function(x1){F(c(x1,1e40),0) } > Fy=function(x2){Fx(x2)}
Consider the following dataset,
hsb2=read.table("http://freakonometrics.free.fr/hsb2.csv", header=TRUE, sep=",") math_male=hsb2$math[female==0] write_male=hsb2$write[female==0] math_female=hsb2$math[female==1] write_female=hsb2$write[female==1] plot(math_female, write_female, type="p", pch=19,col="red",xlab="maths",ylab="writing",cex=.8) points(math_male, write_male, cex=1.2, col="blue")
with here maths versus writing, with girls in red and boys in blue, where variables here are
female :
0: male
1: female
race :
1: hispanic
2: asian
3: african-amer
4: white
ses :
1: low
2: middle
3: high
schtyp : type of school
1: public
2: private
prog : type of program
1: general
2: academic
3: vocation
read : reading score
write : writing score
math : math score
science : science score
socst : social studies score
We can try to understand correlation between math and writing skills. Covariates can be the sex of the child, and his reading skills. The question will then be: are good students in maths and writing simply students that can read well ?
Here the code is simply
> W=hsb2$write>=50 > M=hsb2$math>=50 > base=data.frame(Y1=W,Y2=M, + X1=hsb2$female,X2=hsb2$read) > > library(Zelig) > REG= zelig(list(mu1=Y1~X1+X2, + mu2=Y2~X1+X2, + rho=~1), + model="bprobit",data=base) > summary(REG) Call: zelig(formula = list(mu1 = Y1 ~ X1 + X2, mu2 = Y2 ~ X1 + X2, rho = ~1), model = "bprobit", data = base) Pearson Residuals: Min 1Q Median 3Q Max probit(mu1) -4.7518 -0.502594 0.15038 0.53038 1.8592 probit(mu2) -3.4243 -0.653537 0.23673 0.67011 2.6072 rhobit(rho) -4.9821 0.010481 0.13500 0.40776 2.9171 Coefficients: Value Std. Error t value (Intercept):1 -5.484711 0.787101 -6.96825 (Intercept):2 -4.061384 0.633781 -6.40818 (Intercept):3 1.332187 0.322175 4.13497 X1:1 1.125924 0.233550 4.82092 X1:2 0.167258 0.202498 0.82598 X2:1 0.103997 0.014662 7.09286 X2:2 0.082739 0.012026 6.88017 Number of linear predictors: 3 Names of linear predictors: probit(mu1), probit(mu2), rhobit(rho) Dispersion Parameter for binom2.rho family: 1 Residual Deviance: 364.51 on 593 degrees of freedom Log-likelihood: -182.255 on 593 degrees of freedom Number of Iterations: 3 > (exp(summary(REG)@coef3[3])-1)/(exp( summary(REG)@coef3[3])+1) [1] 0.5824045
with a remaining correlation among residuals of 0.58. So with only the sex of the student, and his or her reading skill, we cannot explain the correlation between maths and writing skills. With our previous code, we have here
> beta0=c((exp(summary(REG)@coef3[3])-1)/(exp(summary(REG)@coef3[3])+1), + summary(REG)@coef3[c(1:2,4:7),1]) > beta0 (Intercept):1 (Intercept):2 X1:1 X1:2 0.58240446 -5.48471133 -4.06138412 1.12592427 0.16725842 X2:1 X2:2 0.10399668 0.08273879 > (OPT=optim(fn=logV,par=beta0,method="BFGS")$par) (Intercept):1 (Intercept):2 X1:1 X1:2 0.5824045 -5.4847113 -4.0613841 1.1259243 0.1672584 X2:1 X2:2 0.1039967 0.0827388
i.e. we obtain (almost) exactly the same estimators. But here I have used as starting values for the optimization procedure the estimators given by R. If we change them, hopefully we have a robust maximum likelihood estimator,
> (OPT=optim(fn=logV,par=beta0/2,method="BFGS")$par) (Intercept):1 (Intercept):2 X1:1 X1:2 0.58233360 -5.49428984 -4.06839571 1.12696594 0.16760347 X2:1 X2:2 0.10417767 0.08287409 There were 12 warnings (use warnings() to see them)
So once again, it is possible to optimize numerically a likelihood function, and it works.
Histoire de poursuivre le billet sur la régression logistique (ou logit, ici), je continue avec la régression probit. Chester Bliss (papier de 1934 en ligne ici) travaillait, en tant que biologiste, sur la conception de pesticides. Au lieu de travailler (comme habituellement) sur le pourcentage de nuisibles détruits, mais sur une grandeur qu’il a appelé “probability unit“, soit “prob…it“, qu’il condensa en probit. De manière arbitraire, un pourcentage de tués valant 0,0001 correspondait à une probit nulle, et un pourcentage de 0,9999 à une probit de 10.
Il avait d’ailleurs proposé une table de correspondance, permettant de passer du pourcentage au probit. Il proposait alors de relier le probit au logarithme de la dose de pesticides produits, ce qui devait aboutir – dû moins l’espérait-il – à une ligne droite. Il appela ce modèle un modèle probit.
David Finney (là pour un article rétrospectif) a poursuivi cette idée, toujours dans le domaine toxicologique. Dans son livre de 1952, on retrouve une table de convertion associée à cette grandeur introduite par Bliss
Mais pour aller plus loin, il faut remonter à un papier publié par Finney et Stevens en 1948, où ils eurent la bonne idée de renormer les grandeurs de Bliss en translatant tout de 5, autrement dit un pourcentage quasi-nul (0,0001) correspondait à une probit de -5, et un pourcentage de 0,9999 à une probit de 5. Aussi la probit de 0 correspondait au cas où le pourcentage valait 1/2. Ils notent ainsi que la probit Y associée à une proportion P est la grandeur qui vérifie
Voilà pour la petite histoire…