R/cgaps.R
cgaps.Rd
Calculates maximum likelihood estimates of the extremal index \(\theta\) based on the \(K\)-gaps model for threshold inter-exceedances times of Holesovsky and Fusek (2019).
cgaps(data, u, D = 0, inc_cens = FALSE)
data | A numeric vector of raw data. No missing values are allowed. |
---|---|
u | A numeric scalar. Extreme value threshold applied to data. |
D | A numeric scalar. Censoring parameter \(D\). |
inc_cens | A logical scalar indicating whether or not to include contributions from censored inter-exceedance times relating to the first and last observation. |
An object (a list) of class c("cgaps", "exdex")
containing
theta
The maximum likelihood estimate (MLE) of \(\theta\).
se
The estimated standard error of the MLE.
ss
The list of summary statistics returned from
kgaps_stat
.
D, u, inc_cens
The input values of D
,
u
and inc_cens
.
call
The call to cgaps
.
Add details.
kgaps
for maximum likelihood estimation of the
extremal index \(\theta\) using the \(K\)-gaps model.
kgaps_stat
for the calculation of sufficient
statistics for the \(K\)-gaps model.
spm
for estimation of the extremal index
\(\theta\) using a semiparametric maxima method.
iwls
: iterated weighted least squares estimator.
#> #> Call: #> kgaps(data = sp500, u = u) #> #> Estimate of the extremal index theta: #> [1] 0.6953summary(theta)#> #> Call: #> kgaps(data = sp500, u = u) #> #> Estimate Std. Error #> theta 0.6953 0.007234#> #> Call: #> kgaps(data = newlyn, u = u, k = 2) #> #> Estimate of the extremal index theta: #> [1] 0.1756summary(theta)#> #> Call: #> kgaps(data = newlyn, u = u, k = 2) #> #> Estimate Std. Error #> theta 0.1756 0.009216