cvselect {mev}

Threshold selection via coefficient of variation

Description

This function computes the empirical coefficient of variation and computes a weighted statistic comparing the squared distance with the theoretical coefficient variation corresponding to a specific shape parameter (estimated from the data using a moment estimator as the value minimizing the test statistic, or using maximum likelihood). The procedure stops if there are no more than 10 exceedances above the highest threshold

Usage

cvselect(
  xdat,
  thresh,
  method = c("mle", "wcv", "cv"),
  nsim = 999L,
  nthresh = 10L,
  level = 0.05,
  lazy = FALSE
)

Arguments

xdat

[vector] vector of observations

thresh

[vector] vector of threshold. If missing, set to p^k for k=0 to k=nthresh

method

[string], either moment estimator for the (weighted) coefficient of variation (wcv and cv) or maximum likelihood (mle)

nsim

[integer] number of bootstrap replications

nthresh

[integer] number of thresholds, if thresh is not supplied by the user

level

[numeric] probability level for sequential testing procedure

lazy

[logical] compute the bootstrap p-value until the test stops rejecting at level level? Default to FALSE

Value

a list with elements

• thresh: value of threshold returned by the procedure, NA if the hypothesis is rejected at all thresholds

• cthresh: sorted vector of candidate thresholds

• cindex: index of selected threshold among cthresh or NA if none returned

• pval: bootstrap p-values, with NA if lazy and the p-value exceeds level at lower thresholds

• shape: shape parameter estimates

• nexc: number of exceedances of each threshold cthresh

• method: estimation method for the shape parameter

References

del Castillo, J. and M. Padilla (2016). Modelling extreme values by the residual coefficient of variation, SORT, 40(2), pp. 303–320.