Last week, in our *mathematical statistics *course, we’ve seen the law of large numbers (that was proven in the *probability *course), claiming that

given a collection of i.i.d. random variables, with

To visualize that convergence, we can use

> m=100 > mean_samples=function(n=10){ + X=matrix(rnorm(n*m),nrow=m,ncol=n) + return(apply(X,1,mean)) + } > B=matrix(NA,100,20) > for(i in 1:20){ + B[,i]=mean_samples(i*10) + } > colnames(B)=as.character(seq(10,200,by=10)) > boxplot(B)

It is possible to visualize also the bounds (used in the central limit theorem to get a limiting non degenerated distribution)

> u=seq(0,21,by=.2) > v=sqrt(u*10) > lines(u,1.96/v,col="red") > lines(u,-1.96/v,col="red")

Yesterday, we’ve been discussing properties of the empirical cumulative distribution function,

We’ve seen Glivenko-Cantelli theorem, which states that (under mild assumptions)

To visualize that convergence use the following code. Here I use the trick

to get the maximum (componentwise) between two matrices

> m=100 > inf_sample=function(n=10){ + X=matrix(rnorm(n*m),nrow=m,ncol=n) + Xs=t(apply(X,1,sort)) + Pe_inf=matrix(rep((0:(n-1))/n, + each=m),nrow=m,ncol=n) + Pe_sup=matrix(rep((0:n)/n,each=m), + nrow=m,ncol=n) + Pt=pnorm(Xs) + D1=abs(Pe_inf-Pt) + D2=abs(Pe_sup-Pt) + Df=(D1+D2)/2+abs(D2-D1)/2 + return(apply(Df,1,max)) + } > B=matrix(NA,100,20) > for(i in 1:20){ + B[,i]=inf_sample(i*10) + } > colnames(B)=as.character(seq(10,200,by=10)) > boxplot(B)

We have also discussed the pointwise asymptotic normality of the empirical cumulative distribution function

Here again, it is possible to visualize it. The first step is to compute several trajectories for empirical cumulative distribution function

> u=seq(-3,3,by=.1) > plot(u,u,ylim=c(0,1),col="white") > M=matrix(NA,length(u),1000) > for(m in 1:1000){ + n=100 + x=rnorm(n) + Femp=Vectorize(function(t) mean(x<=t)) + v=Femp(u) + M[,m]=v + lines(u,v,col='light blue',type="s") + }

Note that we can compute (pointwise) confidence bands

> lines(u,apply(M,1,mean),col="red",type="l") > lines(u,apply(M,1,function(x) quantile(x,.05)), + col="red",type="s") > lines(u,apply(M,1,function(x) quantile(x,.95)), + col="red",type="s")

Now, if we focus on one specific point, we can visualize the asmptotic normality (i.e. the almost normality when we have a sample of size 100)

> x0=-1 > y=M[which(u==x0),] > hist(y,probability=TRUE, + breaks=seq(.015,0.55,by=.01)) > vu=seq(0,1,by=.001) > lines(vu,dnorm(vu,pnorm(x0), + sqrt((pnorm(x0)*(1-pnorm(x0)))/100)), + col="red")

If you want the componentwise maximum, why not use pmax? For matrices A and B of appropriate dimensions, pmax(A,B) does exactly that.

thanks for the tip, I was not sure…