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Parametric Bootstrap CIs for GPD Return Levels

ci_parametric_bootstrap.Rd

Simulate from a fitted GPD, refit, and compute return levels to obtain parametric bootstrap confidence intervals.

Usage

ci_parametric_bootstrap(
  gpd_fit,
  n_boot = 500,
  return_periods = c(1, 5, 10),
  conf_level = 0.95,
  n_obs_per_year = 8760,
  seed = 42
)

Arguments

gpd_fit

A list with elements u, scale, shape, n_exceed (as returned by the per-station GPD targets).

n_boot

Number of bootstrap replicates (default 500).

return_periods

Numeric vector of return periods in years.

conf_level

Confidence level (default 0.95).

n_obs_per_year

Observations per year (default 8760).

seed

Random seed (default 42).

Value

A data.frame with columns: return_period, return_level, lower, upper, n_success, method.

Details

For each replicate:

  1. Simulate n_exceed exceedances from GPD(scale, shape)

  2. Add threshold to obtain values above u

  3. Refit GPD via mev::fit.gpd()

  4. Compute return levels

Uses the percentile method for CIs.