fitMcGPD: Fitting Markov Chain Models to Peaks Over a Threshold

In POT: Generalized Pareto Distribution and Peaks Over Threshold

Fitting Markov Chain Models to Peaks Over a Threshold

Description

Fitting a Markov chain to cluster exceedances using a bivariate extreme value distribution and a censored maximum likelihood procedure.

Usage

fitmcgpd(data, threshold, model = "log", start, ..., std.err.type =
"observed", corr = FALSE, warn.inf = TRUE, method = "BFGS")

Arguments

data	A vector of observations.
threshold	The threshold value.
model	A character string which specifies the model used. Must be one of log (the default), alog, nlog, anlog, mix and amix for the logistic, asymmetric logistic, negative logistic, asymmetric negative logistic, mixed and asymmetric mixed models.
start	Optional. A list for starting values in the fitting procedure.
...	Additional parameters to be passed to the optim function or to the bivariate model. In particular, parameter of the model can be hand fixed.
std.err.type	The type of the standard error. Currently, one must specify ``observed'' for observed Fisher information matrix or ``none'' for no computations of the standard errors.
corr	Logical. Should the correlation matrix be computed?
warn.inf	Logical. Should users be warned if likelihood is not finite at starting values?
method	The optimization method, see optim.

Details

The Markov Chain model is defined as follows:

f(x_1;theta_1) f(x_2 | x_1;theta_1, theta_2) … f(x_n | x_{n-1}; theta_1, theta_2)

As exceedances above a (high enough) threshold are of interest, it is assumed that the marginal are GPD distributed, while the joint distribution is represented by a bivariate extreme value distribution. Smith et al. (1997) present theoretical results about this Markov Chain model.

The bivariate exceedances are fitted using censored likelihood procedure. This methodology is fully described in Ledford (1996).

Most of models are described in Kluppelberg (2006).

Value

The function returns an object of class c("mcpot", "uvpot", "pot"). As usual, one can extract several features using fitted (or fitted.values), deviance, logLik and AIC functions.

fitted.values	The maximum likelihood estimates of the Markov chain including estimated parameters of the bivariate extreme value distribution.
std.err	A vector containing the standard errors - only present when the observed information matrix is not singular.
var.cov	The asymptotic variance covariance matrix - only presents when the observed information matrix is not singular.
deviance	The deviance.
corr	The correlation matrix.
convergence, counts, message	Informations taken from the optim function.
threshold	The threshold.
pat	The proportion above the threshold.
nat	The number above the threshold.
data	The observations.
exceed	The exceedances.
call	The call of the current function.
model	The model for the bivariate extreme value distribution.
chi	The chi statistic of Coles (1999). A value near 1 (resp. 0) indicates perfect dependence (resp. independence).

Warnings

Because of numerical problems, there exists artificial numerical constraints imposed on each model. These are:

• For the logistic and asymmetric logistic models: alpha must lie in [0.05, 1] instead of [0,1];

• For the negative logistic model: alpha must lie in [0.01, 15] instead of [0, infinity[;

• For the asymmetric negative logistic model: alpha must lie in [0.2, 15] instead of [0, infinity[;

• For the mixed and asymmetric mixed models: None artificial numerical constraints are imposed.

For this purpose, users must check if estimates are near these artificial numerical constraints. Such cases may lead to substantial biases on the GP parameter estimates. One way to detect quickly if estimates are near the border constraints is to look at the standard errors for the dependence parameters. Small values (i.e. < 1e-5) often indicates that numerical constraints have been reached.

In addition, users must be aware that the mixed and asymmetric mixed models can not deal with perfect dependence.

Thus, user may want to plot the Pickands' dependence function to see if variable are near independence or dependence cases using the pickdep function.

In addition, we recommend to fix the marginal parameters. Indeed, even this is a two steps optimization procedure, this avoid numerical troubles - the likelihood function for the Markov chain model seems to be problematic. Thus, estimates are often better using the two stages approach.

Author(s)

Mathieu Ribatet

References

Kl\"uppelberg, C., and May A. (2006) Bivariate extreme value distributions based on polynomial dependence functions. Mathematical Methods in the Applied Sciences, 29 1467–1480.

Ledford A., and Tawn, J. (1996) Statistics for near Independence in Multivariate Extreme Values. Biometrika, 83 169–187.

Smith, R., and Tawn, J., and Coles, S. (1997) Markov chain models for threshold exceedances. Biometrika, 84 249–268

See Also

The following usual generic functions are available print, plot as well as new generic functions retlev and convassess.

See also pickdep.

For optimization in R, see optim.

Examples

mc <- simmc(1000, alpha = 0.25)
mc <- qgpd(mc, 0, 1, 0.25)
##A first application when marginal parameter are estimated
fitmcgpd(mc, 0)

##Another one where marginal parameters are fixed
fmle <- fitgpd(mc, 0)
fitmcgpd(mc, 0, scale = fmle$param["scale"], shape = fmle$param["shape"])

POT documentation built on April 14, 2022, 5:07 p.m.