AsymVarBR: Asymptotic variance matrix for the Brown-Resnick process.
In tailDepFun: Minimum Distance Estimation of Tail Dependence Models

Description Usage Arguments Details Value References See Also Examples

Computes the asymptotic variance matrix for the Brown-Resnick process, estimated using the pairwise M-estimator or the weighted least squares estimator.

1	AsymVarBR(locations, indices, par, method, Tol = 1e-05)

`locations`	A d x 2 matrix containing the Cartesian coordinates of d points in the plane.
`indices`	A q x d matrix containing exactly 2 ones per row, representing a pair of points from the matrix `locations`, and zeroes elsewhere.
`par`	The parameters of the Brown-Resnick process. Either (α,ρ) for an isotropic process or (α,ρ,β,c) for an anisotropic process.
`method`	Choose between "Mestimator" and "WLS".
`Tol`	For "Mestimator" only. The tolerance in the numerical integration procedure. Defaults to 1e-05.

The parameters of a The matrix indices can be either user-defined or returned from the function selectGrid with cst = c(0,1). Calculation might be rather slow for method = "Mestimator".

A q by q matrix.

Einmahl, J.H.J., Kiriliouk, A., Krajina, A., and Segers, J. (2016). An Mestimator of spatial tail dependence. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 78(1), 275-298.

Einmahl, J.H.J., Kiriliouk, A., and Segers, J. (2018). A continuous updating weighted least squares estimator of tail dependence in high dimensions. Extremes 21(2), 205-233.

selectGrid

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locations <- cbind(rep(1:2, 3), rep(1:3, each = 2))
indices <- selectGrid(cst = c(0,1), d = 6, locations = locations, maxDistance = 1)
AsymVarBR(locations, indices, par = c(1.5,3), method = "WLS")