mable.hellcorr: Estimate of Hellinger Correlation between two random...

In mable: Maximum Approximate Bernstein/Beta Likelihood Estimation

Estimate of Hellinger Correlation between two random variables and Bootstrap

Description

Estimate of Hellinger Correlation between two random variables and Bootstrap

Usage

mable.hellcorr(
  x,
  unif.mar = FALSE,
  pseudo.obs = c("empirical", "mable"),
  M0 = c(1, 1),
  M = c(30, 30),
  search = TRUE,
  mar.deg = TRUE,
  high.dim = FALSE,
  interval = cbind(0:1, 0:1),
  B = 200L,
  conf.level = 0.95,
  integral = TRUE,
  controls = mable.ctrl(sig.level = 0.05),
  progress = FALSE
)

hellcorr(
  x,
  unif.mar = FALSE,
  pseudo.obs = c("empirical", "mable"),
  M0 = c(1, 1),
  M = c(30, 30),
  search = TRUE,
  mar.deg = TRUE,
  high.dim = FALSE,
  interval = cbind(0:1, 0:1),
  B = 200L,
  conf.level = 0.95,
  integral = TRUE,
  controls = mable.ctrl(sig.level = 0.05),
  progress = FALSE
)

Arguments

x	an n x 2 data matrix of observations of the two random variables
unif.mar	logical, whether all the marginals distributions are uniform or not. If not the pseudo observations will be created using empirical or mable marginal distributions.
pseudo.obs	"empirical": use empirical distribution to form pseudo, observations, or "mable": use mable of marginal cdfs to form pseudo observations
M0	a nonnegative integer or a vector of d nonnegative integers specify starting candidate degrees for searching optimal degrees.
M	a positive integer or a vector of d positive integers specify the maximum candidate or the given model degrees for the joint density.
search	logical, whether to search optimal degrees between M0 and M or not but use M as the given model degrees for the joint density.
mar.deg	logical, if TRUE (default), the optimal degrees are selected based on marginal data, otherwise, the optimal degrees are chosen by the method of change-point. See details.
high.dim	logical, data are high dimensional/large sample or not if TRUE, run a slower version procedure which requires less memory
interval	a 2 by 2 matrix, columns are the marginal supports
B	the number of bootstrap samples and number of Monte Carlo runs for estimating p.value of the test for Hellinger correlation = 0 if test=TRUE.
conf.level	confidence level
integral	logical, using "integrate()" or not (Riemann sum)
controls	Object of class mable.ctrl() specifying iteration limit and the convergence criterion eps. Default is mable.ctrl. See Details.
progress	if TRUE a text progressbar is displayed

Details

This function calls mable.copula() for estimation of the copula density.

Value

• eta Hellinger correlation

• CI.eta Bootstrap confidence interval for Hellinger correlation if B>0.

Author(s)

Zhong Guan <[e-mail]>

References

Guan, Z., Nonparametric Maximum Likelihood Estimation of Copula

See Also

mable, mable.mvar, mable.copula

mable documentation built on Oct. 1, 2024, 9:06 a.m.