Moment estimators for the generalized Pareto distribution and parameter estimators based on two quantiles plus a tail index estimator for the hybrid Pareto distribution.
vector of sample for which the parameters will be estimated
an optional a priori value for the tail index parameter
the percentage of largest observations used for tail index estimation
hpareto.mme, the tail index is assumed to be positive. In
case one has some prior information on the value of the tail index
parameter, it is possible to provide this value as an argument to the
hpareto.mme. The two other parameters mu and sigma will
be estimated based on that tail index estimate and two quantiles of the
hybrid Pareto distribution. If the tail index parameter is not provided
as input, it will be estimated with the Hill estimator using data above
p-quantile. By default,
p=0.99 but this might be inappropriate
depending on the sample. Since the tail index estimate is very
sensitive, it is recommended to tune carefully the
A vector of parameter estimates.
Hosking, J. R. M. and Wallis, J. R. (1987), Parameter and quantile estimation for the Generalized Pareto distribution, 29, Technometrics Carreau, J. and Bengio, Y. (2009), A Hybrid Pareto Model for Asymmetric Fat-tailed Data: the Univariate Case, 12, Extremes
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