# Generalized Inverse Gaussian Archimedean Copulas

#### Marius Hofert

#### 2026-02-19

    source(system.file("Rsource", "GIG.R", package="copula"))## ../inst/Rsource/GIG.R
    require(copula)
    require(bbmle)
    require(lattice)
    require(grid)
    source(system.file("Rsource", "utils.R", package="copula", mustWork=TRUE))##-> showProc.time() ..
    doPDF <- FALSE ## set 'do.profile' below -- *visibly*

## 1 Auxiliary functions

    ##' Initial interval for GIG
    ##' @title Initial interval for GIG
    ##' @param U (n x d)-matrix of simulated data
    ##' @param h non-negative auxiliary parameter for computing initial intervals
    ##' @param method "etau" via sample version of Kendall's tau
    ##'               "dmle.G" via DMLE of Gumbel
    ##' @return (2 x 2)-matrix containing the initial interval [1st row: lower,
    ##'         2nd row: upper; 2 parameters => 2 cols]
    ##' @author Marius Hofert
    ii.GIG <- function(U, h, method=c("etau","dmle.G")){
        stopifnot(h >= 0, length(h) >= 2)
        I <- matrix(, nrow=2, ncol=2, dimnames=list(c("lower", "upper"), c("nu", "theta")))
        ## estimate Kendall's tau
        method <- match.arg(method)
        tau.hat <- switch(method,
                          "etau" = { # uses sample version of tau, more accurate but slower
                              tau.hat.mat <- cor(U, method="kendall")
                              mean(tau.hat.mat[upper.tri(tau.hat.mat)])
                          },
                          "dmle.G" = { # uses DMLE for Gumbel to estimate tau
                              Z <- apply(U, 1, max)
                              theta.hat.G <- log(ncol(U))/(log(length(Z))-log(sum(-log(Z))))
                              copGumbel@tau(theta.hat.G)
                          },
                          stop("wrong method:", method))
        ## compute largest value of theta (for upper left endpoint of the inital interval)
        stopifnot(tau.hat > 0)
        nu.min <- 0
        I[1,1] <- nu.min # smallest value for nu
        th.max <- iTau.GIG(max(tau.hat-h[1],0.005), theta=c(nu.min, NA))
        I[2,2] <- th.max[2] # largest value for theta
        ## compute smallest theta (for lower left endpoint of the inital interval)
        th.min <- iTau.GIG(min(tau.hat+h[2],0.995), theta=c(nu.min, NA)) # largest attainable tau with 1e-30 is one.m.eps=0.9602
        I[1,2] <- th.min[2]
        ## compute largest nu (for lower right endpoint of the inital interval)
        nu.max <- iTau.GIG(max(tau.hat-h[1],0.005), theta=c(NA, th.min[2]))
        I[2,1] <- nu.max[1]
        ## result
        I
    }

    ##' -log-likelihood
    ##' @title -log-likelihood
    ##' @param nu parameter of the generator/copula
    ##' @param theta parameter of the generator/copula
    ##' @param u (n x d)-matrix of simulated data
    ##' @return -sum(log(density))
    ##' @author Marius Hofert
    nlogl.GIG <- function(nu, theta, u){
        if(!is.matrix(u)) u <- rbind(u)
        if((d <- ncol(u)) < 2) stop("u should be at least bivariate") # check that d >= 2
        -sum(dacopula.GIG(u, theta=c(nu, theta), n.MC=0, log=TRUE))
    }
    nlogl.GIG. <- function(theta, u) nlogl.GIG(theta[1], theta=theta[2], u=u) # vectorized version

## 2 Setup

Note: The GIG family is two-parametric.

### Plot Kendall’s tau as a function in \(\theta\) for different \(\nu\)

    th1 <- c(0, 0.1, 0.5, 1, 5, 10)
    cols <- colorRampPalette(c("red", "orange", "darkgreen", "turquoise", "blue"),
                             space="Lab")(length(th1))
    par(pty="s")
    for(i in seq_along(th1))
        curve(tau.GIG(cbind(th1[i],x)), 1e-12, 2,
              main="Kendall's tau for the GIG family", ylim=c(0,1),
              xlab=expression(theta), ylab=expression(tau(nu,theta)), add=(i>1),
              lwd=1.4, col=cols[i])
    label <- as.expression(lapply(1:length(th1), function(i) substitute(nu==nu., list(nu.=th1[i]))))
    legend("topright", label, bty="n", lwd=1.4, col=cols)

### Parameter specification

Let’s specify some parameters.

    n <- 100 # sample size
    d <- 10 # dimension
    nu <- 0.2 # fix nu
    tau <- 0.5 # => psi(t)=(1+t)^(-nu/2)besselK(theta*sqrt(1+t), nu=nu)/besselK(theta, nu=nu) with (nu, theta)=(0.2, 0.0838)
    h <- c(0.15, 0.15) # h_-, h_+ (for initial value)

## 3 Sampling and estimation

### Sampling

    theta <- iTau.GIG(tau, c(nu, NA)) # determine theta such that tau is matched (for given nu)
    set.seed(1000)
    U <- rnacopula.GIG(n, d, theta)
    par(pty="s")
    splom2(U, cex=0.4, pscales=0, main=paste("Sample of size",n,
                                  "from a GIG copula"))

### Estimation

    I <- ii.GIG(U, h)
    start <- colMeans(I) # initial interval

    ## 1) Without profiling: optim with method="L-BFGS-B"
    if(FALSE) # << don't do it if won't look at it  -- takes ca.  16.5 sec
    system.time(optim(par=start, method="L-BFGS-B",
                      fn=function(x) nlogl.GIG(x[1], theta=x[2], u=U),
                      lower=c(I[1,1], I[1,2]), upper=c(I[2,1], I[2,2])))

    ## 2) With profiling: via mle (uses optimizer="optim" with method="L-BFGS-B")
    nLL <- function(nu, theta) nlogl.GIG(nu, theta, u=U)
    system.time(ml <- mle(nLL, method="L-BFGS-B",
                          start=list(nu=mean(I[,1]), theta=mean(I[,2])),
                          lower=c(nu=I[1,1], theta=I[1,2]),
                          upper=c(nu=I[2,1], theta=I[2,2])))

    ##    user  system elapsed
    ##   8.856   0.061   8.933

    summary(ml)

    ## Maximum likelihood estimation
    ##
    ## Call:
    ## mle(minuslogl = nLL, start = list(nu = mean(I[, 1]), theta = mean(I[,
    ##     2])), method = "L-BFGS-B", lower = c(nu = I[1, 1], theta = I[1,
    ##     2]), upper = c(nu = I[2, 1], theta = I[2, 2]))
    ##
    ## Coefficients:
    ##         Estimate Std. Error
    ## nu    0.19074320 0.06197105
    ## theta 0.09566594 0.01386170
    ##
    ## -2 log L: -994.5633

    str(ml@details)

    ## List of 6
    ##  $ par        : Named num [1:2] 0.1907 0.0957
    ##   ..- attr(*, "names")= chr [1:2] "nu" "theta"
    ##  $ value      : num -497
    ##  $ counts     : Named int [1:2] 30 30
    ##   ..- attr(*, "names")= chr [1:2] "function" "gradient"
    ##  $ convergence: int 0
    ##  $ message    : chr "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH"
    ##  $ hessian    : num [1:2, 1:2] 264 139 139 5277
    ##   ..- attr(*, "dimnames")=List of 2
    ##   .. ..$ : chr [1:2] "nu" "theta"
    ##   .. ..$ : chr [1:2] "nu" "theta"

    ## 3) With profiling: via mle2 (uses optimizer="optim" with method="L-BFGS-B")
    system.time(ml2 <- mle2(nlogl.GIG, data=list(u=U), method="L-BFGS-B",
                            start=list(nu=mean(I[,1]), theta=mean(I[,2])),
                            lower=c(nu=I[1,1], theta=I[1,2]),
                            upper=c(nu=I[2,1], theta=I[2,2])))

    ##    user  system elapsed
    ##  10.408   0.001  10.437

    summary(ml2)

    ## Maximum likelihood estimation
    ##
    ## Call:
    ## mle2(minuslogl = nlogl.GIG, start = list(nu = mean(I[, 1]), theta = mean(I[,
    ##     2])), method = "L-BFGS-B", data = list(u = U), lower = c(nu = I[1,
    ##     1], theta = I[1, 2]), upper = c(nu = I[2, 1], theta = I[2,
    ##     2]))
    ##
    ## Coefficients:
    ##       Estimate Std. Error z value     Pr(z)
    ## nu    0.190743   0.060871  3.1336  0.001727 **
    ## theta 0.095666   0.013684  6.9910 2.729e-12 ***
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ##
    ## -2 log L: -994.5633

    str(ml2@details)

    ## List of 8
    ##  $ par        : Named num [1:2] 0.1907 0.0957
    ##   ..- attr(*, "names")= chr [1:2] "nu" "theta"
    ##  $ value      : num -497
    ##  $ counts     : Named int [1:2] 30 30
    ##   ..- attr(*, "names")= chr [1:2] "function" "gradient"
    ##  $ convergence: int 0
    ##  $ message    : chr "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH"
    ##  $ hessian    : num [1:2, 1:2] 272 112 112 5387
    ##  $ maxgrad    : num 9.36
    ##  $ eratio     : num 0.0501

## 4 Plots

### Profile likelihood plots

    do.profile <- FALSE # set this to TRUE to compute profile-likelihood plots (time-consuming)
    if(do.profile){
        system.time(prof <- profile(ml))
        if(FALSE) { ## FIXME (?)
            ## maybe this helps: https://stat.ethz.ch/pipermail/r-help/2005-July/076003.html
            ci <- confint(prof)
            ci
            plot(prof)
        }
        system.time(prof2 <- profile(ml2)) # profiling (time-consuming)
        (ci <- confint(prof2))
        plot(prof2) # => for adjusting stepsize etc., see ?profile.mle2
    }
    showProc.time()

    ## Time (user system elapsed): 20.814 0.222 21.081

### -log-likelihood plot

    ## Build grid
    m <- 20 # number of grid points = number of intervals + 1
    th   <- seq(I[1,1], I[2,1], length.out=m) # grid points for nu
    beta <- seq(I[1,2], I[2,2], length.out=m) # grid points for theta
    grid <- expand.grid(theta=th, beta=beta) # grid
    base.saveF <- "GIG_vign-nlogl-gr.rds"
    saveF <- system.file("rData", base.saveF, package = "copula")
    if(nzchar(saveF) && file.exists(saveF)) { # save time, also on CRAN
        val.grid <- readRDS(saveF)
    } else { ## takes around 45 sec
        print(system.time(
            ## val.grid := values of the -log-likelihood on the grid
            val.grid <- apply(grid, 1, nlogl.GIG., u=U)
        ))
        saveF <- file.path(if(dir.exists(sd <- "~/R/Pkgs/copula/inst/rData")) sd
                           else tempdir(), base.saveF)
        saveRDS(val.grid, file = saveF)
        cat("saved to saveFile = ", dQuote(saveF), "\n")
    }
    showProc.time()

    ## Time (user system elapsed): 0.022 0.024 0.046

### Wireframe

    true.theta <- theta
    true.val <- c(true.theta, nlogl.GIG.(true.theta, u=U)) # theoretical optimum
    opt <- ml@coef # optimizer-optimum
    opt.val <- c(opt, nlogl.GIG.(opt, u=U)) # optimizer-optimum and its value
    pts <- rbind(true.val, opt.val) # points to add to wireframe plot
    title <- "-log-likelihood of an Archimedean GIG copula" # title
    sub <- substitute(italic(n) == N ~~~  italic(d)== D ~~~
                      tau == TAU ~~~ "#{eval}:" ~ NIT,
                      list(N=n, D=d, TAU= tau, NIT= ml@details$counts[[1]]))
    sub <- as.expression(sub) # lattice bug
    wireframe(val.grid ~ grid[,1] * grid[,2], screen=list(z=70, x=-55), zoom=0.95,
              xlab = expression(italic(theta)), ylab = expression(italic(beta)),
              zlab = list(as.expression(-log~L * group("(",list(theta,beta),")")), rot=90),
              main=title, sub=sub, pts=pts, scales=list(col=1, arrows=FALSE),
              par.settings=list(axis.line=list(col="transparent"),
              clip=list(panel="off")), zlim=c(min(val.grid, pts[,3]),
                                       max(val.grid, pts[,3])), aspect=1,
              panel.3d.wireframe = function(x,y,z,xlim,ylim,zlim,xlim.scaled,
                                            ylim.scaled,zlim.scaled,pts,...) {
                  panel.3dwire(x=x, y=y, z=z, xlim=xlim, ylim=ylim, zlim=zlim,
                               xlim.scaled=xlim.scaled, ylim.scaled=ylim.scaled,
                               zlim.scaled=zlim.scaled, alpha.regions=0.8, ...)
                  panel.3dscatter(x=pts[,1], y=pts[,2], z=pts[,3],
                                  xlim=xlim, ylim=ylim, zlim=zlim,
                                  xlim.scaled=xlim.scaled, ylim.scaled=ylim.scaled,
                                  zlim.scaled=zlim.scaled, type="p", col=c("red","blue"),
                                  pch=c(3,4), lex=2, cex=1.4, .scale=TRUE, ...)
              },
              key = list(x=0.64, y=1.01,
                         points = list(pch=c(3,4), col=c("red","blue"), lwd=2, cex=1.4),
                         text = list(c("True value", "Optimum of optimizer")), padding.text=3,
                         cex=1, align=TRUE, transparent=TRUE))

### Levelplot

    xlim. <- c(min(grid[,1]),max(grid[,1]))
    ylim. <- c(min(grid[,2]),max(grid[,2]))
    xeps <- (xlim.[2] - xlim.[1]) * 0.04
    yeps <- (ylim.[2] - ylim.[1]) * 0.04
    cols <- adjustcolor(colorRampPalette(c("darkgreen", "green", "orange", "yellow"),
                                         space="Lab")(100), 0.8)
    levelplot(val.grid ~ grid[,1] * grid[,2],
              par.settings = list(layout.heights=list(main=3, sub=2),
                                  regions=list(col=cols)),
              xlim = c(xlim.[1]-xeps, xlim.[2]+xeps),
              ylim = c(ylim.[1]-yeps, ylim.[2]+yeps),
              xlab = expression(italic(theta)), ylab=expression(italic(beta)),
              main=title, sub=sub, pts=pts, aspect=1,
              scales=list(alternating=c(1,1), tck=c(1,0)), contour=TRUE,
              panel = function(x, y, z, pts, ...) {
                  panel.levelplot(x=x, y=y, z=z, ...)
                  grid.points(x=pts[1,1], y=pts[1,2], pch=3,
                              gp=gpar(lwd=2, col="red")) # + true value
                  grid.points(x=pts[2,1], y=pts[2,2], pch=4,
                              gp=gpar(lwd=2, col="blue")) # x optimum
              },
              key = list(x=0.18, y=1.08, points = list(pch=c(3,4), col=c("red","blue"),
                                                       lwd=2, cex=1.4),
                         columns=2, text = list(c("True value", "Optimum of optimizer")),
                         align=TRUE, transparent=TRUE))

    showProc.time()

    ## Time (user system elapsed): 0.636 0.047 0.683