Today, we will go further on the inference of copula functions. Some codes (and references) can be found on a previous post, on nonparametric estimators of copula densities (among other related things). Consider (as before) the loss-ALAE dataset (since we’ve been working a lot on that dataset)

> library(MASS) > library(evd) > X=lossalae > U=cbind(rank(X[,1])/(nrow(X)+1),rank(X[,2])/(nrow(X)+1))

The standard tool to plot nonparametric estimators of densities is to use multivariate kernels. We can look at the density using

> mat1=kde2d(U[,1],U[,2],n=35) > persp(mat1$x,mat1$y,mat1$z,col="green", + shade=TRUE,theta=s*5, + xlab="",ylab="",zlab="",zlim=c(0,7))

or level curves (isodensity curves) with more detailed estimators (on grids with shorter steps)

> mat1=kde2d(U[,1],U[,2],n=101) > image(mat1$x,mat1$y,mat1$z,col= + rev(heat.colors(100)),xlab="",ylab="") > contour(mat1$x,mat1$y,mat1$z,add= + TRUE,levels = pretty(c(0,4), 11))

Kernels are nice, but we clearly observe some border bias, extremely strong in corners (the estimator is 1/4th of what it should be, see another post for more details). Instead of working on sample on the unit square, consider some transformed sample , where is a given function. E.g. a quantile function of an unbounded distribution, for instance the quantile function of the distribution. Then, we can estimate the density of the transformed sample, and using the inversion technique, derive an estimator of the density of the initial sample. Since the inverse of a (general) function is not that simple to compute, the code might be a bit slow. But it does work,

> gaussian.kernel.copula.surface <- function (u,v,n) { + s=seq(1/(n+1), length=n, by=1/(n+1)) + mat=matrix(NA,nrow = n, ncol = n) + sur=kde2d(qnorm(u),qnorm(v),n=1000, + lims = c(-4, 4, -4, 4)) + su<-sur$z + for (i in 1:n) { + for (j in 1:n) { + Xi<-round((qnorm(s[i])+4)*1000/8)+1; + Yj<-round((qnorm(s[j])+4)*1000/8)+1 + mat[i,j]<-su[Xi,Yj]/(dnorm(qnorm(s[i]))* + dnorm(qnorm(s[j]))) + } + } + return(list(x=s,y=s,z=data.matrix(mat))) + }

Here, we get

Note that it is possible to consider another transformation, e.g. the quantile function of a Student-t distribution.

> student.kernel.copula.surface = + function (u,v,n,d=4) { + s <- seq(1/(n+1), length=n, by=1/(n+1)) + mat <- matrix(NA,nrow = n, ncol = n) + sur<-kde2d(qt(u,df=d),qt(v,df=d),n=5000, + lims = c(-8, 8, -8, 8)) + su<-sur$z + for (i in 1:n) { + for (j in 1:n) { + Xi<-round((qt(s[i],df=d)+8)*5000/16)+1; + Yj<-round((qt(s[j],df=d)+8)*5000/16)+1 + mat[i,j]<-su[Xi,Yj]/(dt(qt(s[i],df=d),df=d)* + dt(qt(s[j],df=d),df=d)) + } + } + return(list(x=s,y=s,z=data.matrix(mat))) + }

Another strategy is to consider kernel that have precisely the unit interval as support. The idea is here to consider the product of Beta kernels, where parameters depend on the location

> beta.kernel.copula.surface= + function (u,v,bx=.025,by=.025,n) { + s <- seq(1/(n+1), length=n, by=1/(n+1)) + mat <- matrix(0,nrow = n, ncol = n) + for (i in 1:n) { + a <- s[i] + for (j in 1:n) { + b <- s[j] + mat[i,j] <- sum(dbeta(a,u/bx,(1-u)/bx) * + dbeta(b,v/by,(1-v)/by)) / length(u) + } + } + return(list(x=s,y=s,z=data.matrix(mat))) + }

On those two graphs, we can clearly observe strong tail dependence in the upper (right) corner, that cannot be intuited using a standard kernel estimator…

Cite this blog post

Arthur Charpentier (2012, September 20). (nonparametric) copula density estimation. *Freakonometrics*. Retrieved May 23, 2024, from https://doi.org/10.58079/oumm

Great, what if we want to extend this code in three dimensions is it directly or there are things to put into account?

Fix broken links, please, thanks.

The code at the top of this posting provoked a question on R-help because the argument to theta in function persp throws an error:

persp(mat1$x,mat1$y,mat1$z,col=”green”,

shade=TRUE,theta=s*5,

xlab=””,ylab=””,zlab=””,zlim=c(0,7))

The person who tried to use this code thought `s` was the smoothing function in mgcv, but clearly that makes no sense. I suspect it was a viewing angle done in a loop to create that spinning illustration but a naive user is going to be tripped up. It’s not clear why the questioner did not post here, but perhaps this message will get read. and the code updated? (Removing theta=s*5 allows success.)

Dear Prof Charpentier,

(Nonparametric) Copulas Density Estimation – September 20, 2012

Thank for sharing very clear step on Nonparametric Copulas. FYI, I’m new user of R and now studying copula analysis. I managed to produce the first two plots on kernel but not the rest of it neither gaussian.kernel.copula.surface nor student-t/beta. Please advice.

Thank you.

Rica