Pointwise confidence intervals by bootstrap
Pointwise quantiles and survival probabilities confidence intervals using bootstrap.
bootCI(X, weights = rep(1, length(X)), probs = 1:(length(X) - 1)/length(X), xgrid = sort(X), B = 100, alpha = 0.05, type = "quantile", CritVal = 10, initprop = 1/10, gridlen = 100, r1 = 1/4, r2 = 1/20, plot = F)
X |
a numeric vector of data values. |
weights |
a numeric vector of weights. |
probs |
used if type = "quantile", a numeric vector of probabilities with values in [0,1]. |
xgrid |
used if type = "survival", a numeric vector with values in the domain of X. |
B |
an integer giving the number of bootstrap iterations. |
alpha |
the type 1 error of the bootstrap (1-alpha)-confidence interval. |
type |
type is either "quantile" or "survival". |
CritVal |
a critical value associated to the kernel function given by |
gridlen, initprop, r1, r2 |
parameters used in the function hill.adapt (see |
plot |
If |
Generate B samples of X with replacement to estimate the quantiles of orders probs or the survival probability corresponding to xgrid. Determine the bootstrap pointwise (1-alpha)-confidence interval for the quantiles or the survival probabilities.
LowBound |
the lower bound of the bootstrap (1-alpha)-confidence interval. |
UppBound |
the upper bound of the bootstrap (1-alpha)-confidence interval of level. |
X <- abs(rcauchy(400)) hh <- hill.adapt(X) probs <- probgrid(0.1, 0.999999, length = 100) B <- 200 ## Not run: #For computing time purpose bootCI(X, weights = rep(1, length(X)), probs = probs, B = B, plot = TRUE) xgrid <- sort(sample(X, 100)) bootCI(X, weights = rep(1, length(X)), xgrid = xgrid, type = "survival", B = B, plot = TRUE) ## End(Not run)
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