# abvnonpar: Non-parametric Estimates for Dependence Functions of the... In evd: Functions for Extreme Value Distributions

## Description

Calculate or plot non-parametric estimates for the dependence function A of the bivariate extreme value distribution.

## Usage

 ```1 2 3 4 5 6``` ```abvnonpar(x = 0.5, data, epmar = FALSE, nsloc1 = NULL, nsloc2 = NULL, method = c("cfg", "pickands", "tdo", "pot"), k = nrow(data)/4, convex = FALSE, rev = FALSE, madj = 0, kmar = NULL, plot = FALSE, add = FALSE, lty = 1, lwd = 1, col = 1, blty = 3, blwd = 1, xlim = c(0, 1), ylim = c(0.5, 1), xlab = "t", ylab = "A(t)", ...) ```

## Arguments

 `x` A vector of values at which the dependence function is evaluated (ignored if plot or add is `TRUE`). A(1/2) is returned by default since it is often a useful summary of dependence. `data` A matrix or data frame with two columns, which may contain missing values. `epmar` If `TRUE`, an empirical transformation of the marginals is performed in preference to marginal parametric GEV estimation, and the `nsloc` arguments are ignored. `nsloc1, nsloc2` A data frame with the same number of rows as `data`, for linear modelling of the location parameter on the first/second margin. The data frames are treated as covariate matrices, excluding the intercept. A numeric vector can be given as an alternative to a single column data frame. `method` The estimation method (see Details). Typically either `"cfg"` (the default) or `"pickands"`. The method `"tdo"` performs poorly and is not recommended. The method `"pot"` is for peaks over threshold modelling where only large data values are used for estimation. `k` An integer parameter for the `"pot"` method. Only the largest `k` values are used, as described in `bvtcplot`. `convex` Logical; take the convex minorant? `rev` Logical; reverse the dependence function? This is equivalent to evaluating the function at `1-x`. `madj` Performs marginal adjustments for the `"pickands"` method (see Details). `kmar` In the rare case that the marginal distributions are known, specifies the GEV parameters to be used instead of maximum likelihood estimates. `plot` Logical; if `TRUE` the function is plotted. The x and y values used to create the plot are returned invisibly. If `plot` and `add` are `FALSE` (the default), the arguments following `add` are ignored. `add` Logical; add to an existing plot? The existing plot should have been created using either `abvnonpar` or `abvevd`, the latter of which plots (or calculates) the dependence function for a number of parametric models. `lty, blty` Function and border line types. Set `blty` to zero to omit the border. `lwd, blwd` Function and border line widths. `col` Line colour. `xlim, ylim` x and y-axis limits. `xlab, ylab` x and y-axis labels. `...` Other high-level graphics parameters to be passed to `plot`.

## Details

The dependence function A() of the bivariate extreme value distribution is defined in `abvevd`. Non-parametric estimates are constructed as follows. Suppose (z_{i1},z_{i2}) for i=1,…,n are n bivariate observations that are passed using the `data` argument. If `epmar` is `FALSE` (the default), then the marginal parameters of the GEV margins are estimated (under the assumption of independence) and the data is transformed using

y_{i1} = {1 + s'_1(z_{i1}-a'_1)/b'_1}^(-1/s'_1)

and

y_{i2} = {1 + s'_2(z_{i2}-a'_2)/b'_2}^(-1/s'_2)

for i = 1,…,n, where (a'_1,b'_1,s'_1) and (a'_2,b'_2,s'_2) are the maximum likelihood estimates for the location, scale and shape parameters on the first and second margins. If `nsloc1` or `nsloc2` are given, the location parameters may depend on i (see `fgev`).

Two different estimators of the dependence function can be implemented. They are defined (on 0 <= w <= 1) as follows.

`method = "cfg"` (Caperaa, Fougeres and Genest, 1997)

log(A_c(w)) = 1/n { sum_{i=1}^n log (max[(1-w)y_{i1}, wy_{i1}]) - (1-w)sum_{i=1}^n y_{i1} - w sum_{i=1}^n y_{i2} }

`method = "pickands"` (Pickands, 1981)

A_p(w) = n / {sum_{i=1}^n min[y_{i1}/w, y_{i2}/(1-w)]}

Two variations on the estimator A_p() are also implemented. If the argument `madj = 1`, an adjustment given in Deheuvels (1991) is applied. If the argument `madj = 2`, an adjustment given in Hall and Tajvidi (2000) is applied. These are marginal adjustments; they are only useful when empirical marginal estimation is used.

Let A_n() be any estimator of A(). None of the estimators satisfy max(w,1-w) <= A_n(w) <= 1 for all 0 <= w <= 1. An obvious modification is

A'_n(w) = min(1, max{A_n(w), w, 1-w}).

This modification is always implemented.

Convex estimators can be derived by taking the convex minorant, which can be achieved by setting `convex` to `TRUE`.

## Value

`abvnonpar` calculates or plots a non-parametric estimate of the dependence function of the bivariate extreme value distribution.

## Note

I have been asked to point out that Hall and Tajvidi (2000) suggest putting a constrained smoothing spline on their modified Pickands estimator, but this is not done here.

## References

Caperaa, P. Fougeres, A.-L. and Genest, C. (1997) A non-parametric estimation procedure for bivariate extreme value copulas. Biometrika, 84, 567–577.

Pickands, J. (1981) Multivariate extreme value distributions. Proc. 43rd Sess. Int. Statist. Inst., 49, 859–878.

Deheuvels, P. (1991) On the limiting behaviour of the Pickands estimator for bivariate extreme-value distributions. Statist. Probab. Letters, 12, 429–439.

Hall, P. and Tajvidi, N. (2000) Distribution and dependence-function estimation for bivariate extreme-value distributions. Bernoulli, 6, 835–844.

`abvevd`, `amvnonpar`, `bvtcplot`, `fgev`

## Examples

 ```1 2 3 4 5 6 7``` ```bvdata <- rbvevd(100, dep = 0.7, model = "log") abvnonpar(seq(0, 1, length = 10), data = bvdata, convex = TRUE) abvnonpar(data = bvdata, method = "pick", plot = TRUE) M1 <- fitted(fbvevd(bvdata, model = "log")) abvevd(dep = M1["dep"], model = "log", plot = TRUE) abvnonpar(data = bvdata, add = TRUE, lty = 2) ```

### Example output  ```  1.0000000 0.9378920 0.8836968 0.8483179 0.8192258 0.8092303 0.8219573
 0.8546290 0.9102210 1.0000000
```

evd documentation built on May 1, 2019, 10:11 p.m.