hpareto.negloglike: Hybrid Pareto Maximum Likelihood Estimation

In condmixt: Conditional Density Estimation with Neural Network Conditional Mixtures

Description

Negative log-likelihood and gradient (hpareto.negloglike) and MLE of a hybrid Pareto distribution parameters (hpareto.fit). hpareto.fit applies the optimizer nlm to minimize the negative log-likelihood based on some starting values for the hybrid Pareto parameters.

Usage

hpareto.negloglike(params, x)
hpareto.fit(params, x, ...)

Arguments

params	vector of length 3 of hybrid Pareto parameters c(xi, mu, sigma)
x	a vector of length n of observations assumed to be sampled from a hybrid Pareto distribution
...	optional arguments for nlm called by hpareto.fit

Value

hpareto.negloglike returns a single value (the negative log-likelihood for given parameters and sample) and a vector, the gradient, which is passed as an attribute, while hpareto.fit returns a vector of three parameters, the MLE of the parameters of the hybrid Pareto distribution

Author(s)

Julie Carreau

References

Carreau, J. and Bengio, Y. (2009), A Hybrid Pareto Model for Asymmetric Fat-tailed Data: the Univariate Case, 12, Extremes

See Also

hpareto.mme, dhpareto,phpareto and rhpareto

Examples

r <- rhpareto(500,0.2,trunc=FALSE)
params.init <- hpareto.mme(r)
hpareto.negloglike(params.init,r)
hpareto.fit(params.init,r)

condmixt documentation built on July 1, 2020, 6:04 p.m.