extRemes (version 1.61)

decluster.runs: Declustering Extremes

Description

Performs runs/intervals declustering.

Usage

decluster.runs(z, r)
decluster.intervals(z, ei)

Arguments

z

Logical vector indicating which positions correspond to extreme values.

r

Integer run length.

ei

Estimate of the extremal index.

Value

• A list containing
• scheme
  Name of declustering scheme.
• par
  Value of declustering parameter (i.e., run length).
• nc
  Number of clusters.
• size
  Vector of cluster sizes.
• s
  Vector of times of extremes.
• cluster
  Vector of numbers identifying clusters to which extremes belong.
• t
  Vector of times between extremes.
• inter
  Vector of intercluster time indicators (logical).
• intra
  Vector of intracluster time indicators (logical).

Details

Runs declustering: Extremes separated by fewer than `r' non-extremes belong to the same cluster. Setting `r' < 1 causes each extreme to form a separate cluster.

Intervals declustering: Extremes separated by fewer than `r' non-extremes belong to the same cluster, where `r' is the `nc'-th largest interexceedance time and `nc', the number of clusters, is estimated from the extremal index, `ei', and the times between extremes. Setting `ei' = 1 causes each extreme to form a separate cluster.

References

Smith RL (1989) Extreme value analysis of environmental time series: an application to trend detection in ground-level ozone. Statistical Science 4, 367-393.

Ferro CAT and Segers J (2003) Inference for clusters of extreme values. Journal of the Royal Statistical Society B 65, 545-556.

See Also

exi.intervals

Examples

# Simulate a dependent series of random variables.
x <- runif(1000,-1,1)
x[2:1000] <- x[1:999]*0.6
# -- DON'T RUN
# pacf( x)

# use runs and intervals declustering using the 90th percentile as the threshold.
u <- quantile(x, 0.9)
z <- x > u
exi.intervals(z)
tmp1 <- decluster.runs(z, 1)
tmp2 <- decluster.intervals( z, exi.intervals(z))