| est.tpdm | R Documentation | 
Estimation of tail pairwise dependence matrix (TPDM)
Sub-Routine of est.row.tpdm. Calculates one element of the TPDM
est.tpdm(X, Y = NULL, anz_cores = 1, clust = NULL, q = 0.98)
est.row.tpdm(x, Y, clust = NULL, q = 0.98)
est.element.tpdm(x, y, clust = NULL, q = 0.98)
| X | A t x n dimensional, numeric data-matrix with t: Number of time steps and n: Number of grid points/stations | 
| Y | A t x n dimensional, numeric Data-matrix with t: Number of time steps and n: Number of grid points/stations | 
| anz_cores | Number of cores for parallel computing (default:1); Be careful not to overload your computer! | 
| clust | Optional: If clust = NULL, no declustering is performed. Else, declustering according to cluster-length 'clust'. | 
| q | Threshold for computation of TPDM. Only data above the 'q'-quantile will be used for estimation. Choose such that 0<q<1. | 
| x | Array of length t, where t is the number of time steps | 
| y | Same as x | 
Given a random vector X with components x_{t,i}, x_{t,j} with i,j = 1, \ldots, n and it's radial component r_{t,ij} = \sqrt{x_{t,i}^2 + x_{t,j}^2} and angular components w_{t,i} = x_{t,i}/r_{t,ij} and w_{t,j} = x_{t,j}/r_{t,ij}, the i'th,j'th element of the TPDM is estimated as:
\hat{\sigma}_{ij} = 2 n_{ij,exc}^{-1} \sum_{t=1}^{n} w_{t,i} w_{t,j} |_{(r_{t,ij} > r_{0,ij})} 
.
Given two random vectors X and Y with components x_{t,i}, y_{t,j} with i,j = 1, \ldots, n, and it's radial component  r_{t,ij} = \sqrt{x_{t,i}^2 + y_{t,j}^2} and angular components  w_{t,i}^x = \frac{x_{t,i}}{r_{t,ij}} ; w_{t,j}^y = \frac{y_{t,j}}{r_{t,ij}}, the i'th,j'th element of the cross-TPDM is estimated as:
\hat{\sigma}_{ij} = 2 n^{-1}_{exc} \sum_{t=1}^{n} w^x_{t,i} w^y_{t,j} |_{(r_{t,ij} > r_{0,ij})} 
.
An n x n matrix, containing the estimate of the TPDM
Array containing the estimate of one row of the TPDM.
Value containing the estimate of one element of the TPDM.
Jiang & Cooley (2020) <doi:10.1175/JCLI-D-19-0413.1>; Szemkus & Friederichs (2023)
data    <- precipGER
data.alpha2       <- to.alpha.2(data$pr)
Sigma   <- est.tpdm(data.alpha2,anz_cores =1)
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