Generalized Pareto parameter estimate stability

Uses maximum likelihood estimation to fit a Generalized Pareto (GP) model to threshold excesses over a range of thresholds. The threshold excesses are treated as independent and identically distributed (i.i.d.) observations. The resulting estimates and confidence intervals can be plotted, using plot.stability, to produce a crude graphical diagnostic for threshold choice.

Usage

stability(
  data,
  u_vec,
  prof = FALSE,
  conf = 95,
  mult = 1:2,
  plot_prof = FALSE,
  ...
)

Arguments

data: A numeric vector of observations.
u_vec: A numeric vector of thresholds to be applied to the data. Any duplicated values will be removed. These could be set at sample quantiles of data using quantile.
prof: A logical scalar. Whether to calculate confidence intervals for the GP shape parameter \(\xi\) based on the profile-likelihood for \(\xi\) or using the MLE plus or minus a multiple of the estimated standard error (SE) of the MLE. The intervals produced by the former may be better but they take longer to calculate. Default: FALSE.
conf: A numeric scalar in (0, 100). Confidence level for the confidence intervals. Default: 95%.
mult: A numeric vector of length 2. The range of values over which the profile log-likelihood for \(\xi\) is calculated is (MLE - mult[1] c SE, MLE + mult[2] c SE), where MLE and SE are the MLE and estimated standard error of \(\xi\) and c is the constant for which this interval gives an approximate 100conf% level confidence interval for \(\xi\) when mult = c(1, 1). The default, mult = c(1, 2), works well in most cases. If the routine fails because the range of \(\xi\) is not sufficiently wide then the relevant components of mult should be increased.
plot_prof: A logical scalar. Only relevant if prof = TRUE. If plot_prof = TRUE then the profile log-likelihood is plotted for each threshold. If FALSE then nothing is plotted.
...: Further (optional) arguments to be passed to the optim function for the optimizations on which the profile-likelihood for \(xi\) is based.

Value

An object (list) of class "stability" with components:

ests: MLEs of the GP shape parameter \(\xi\).
ses: Estimated SEs of the MLEs of \(\xi\).
lower: Lower limit of 100conf% confidence intervals for \(\xi\).
upper: Upper limit of 100conf% confidence intervals for \(\xi\).
nexc: The number of threshold excesses.
u_vec: The thresholds supplied by the user.
u_ps: The approximate sample quantiles to which the thresholds in u_vec correspond.
data: The input data.
conf: The input conf.

Each of these components is a numeric vector of length length(u_vec).

Details

For each threshold in u_vec a GP model is fitted by maximum likelihood estimation to the threshold excesses, i.e. the amounts by which the data exceed that threshold. The MLEs of the GP shape parameter \(\xi\) and approximate conf% confidence intervals for \(\xi\) are stored for plotting (by plot.stability) to produce a simple graphical diagnostic to inform threshold selection. This plot is used to choose a threshold above which the underlying GP shape parameter may be approximately constant. See Chapter 4 of Coles (2001). See also the vignette "Introducing threshr".

References

Coles, S. G. (2001) An Introduction to Statistical Modeling of Extreme Values, Springer-Verlag, London. doi:10.1007/978-1-4471-3675-0_3

Examples

# Set a vector of thresholds
u_vec_gom <- quantile(gom, probs = seq(0, 0.9, by = 0.05))

# Symmetric confidence intervals
gom_stab <- stability(data = gom, u_vec = u_vec_gom)
plot(gom_stab)


# Profile-likelihood-based confidence intervals
gom_stab <- stability(data = gom, u_vec = u_vec_gom, prof = TRUE)
#> Fitting at threshold number ...
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 
plot(gom_stab)