recoveryourself_data {cort}R Documentation

Dataset recoveryourself_data

Description

This dataset is a simple test: we simulate random samples from a density inside the piecewise copula class, and test whether or not the estimator can recover it. For that, we will use a 2-dimensional sample with 500 observations, uniform on the unit hypercube, and apply the following function:

h(u) = (u_1, \frac{u_2 + I_{u_1 ≤ \frac{1}{4}} + 2I_{u_1 ≤ \frac{1}{2}} + I_{\frac{3}{4} ≤ u_1}}{4})

Usage

recoveryourself_data

Format

A matrix with 500 rows and 2 columns

The example section below gives the code to re-generate this data if needed.

Details

This dataset is studied in O. Laverny, V. Maume-Deschamps, E. Masiello and D. Rullière (2020).

References

Laverny O, Maume-Deschamps V, Masiello E, Rullière D (2020). “Dependence Structure Estimation Using Copula Recursive Trees.” arXiv preprint arXiv:2005.02912.

Examples

set.seed(seed = 12, kind = "Mersenne-Twister", normal.kind = "Inversion")
x = matrix(runif(1000),500,2)
recoveryourself_data = t(apply(x, 1,function(u){
  if(u[1]< 1/4){
    u[2] = 3/4 + u[2]/4
  } else{ if(u[1]<1/2){
    u[2] = 1/2 + u[2]/4
  } else { if(u[1]<3/4){
    u[2] = u[2]/4
  } else {
    u[2] = 1/4 + u[2]/4
  }}}
  return(u)
}))


[Package cort version 0.3.2 Index]