PPS.fit() returns the fit of a PPS distribution to real data, allowing the scale parameter to be held fixed if desired.
a vector of observations
the method of parameter estimation. It may be "MLE", "iMLE", "OLS", or "LMOM".
the value of the scale parameter, if it is known; if the value is
a list with the initial values of the parameters for some of the estimation methods.
a logical argument to constrain the PPS fit to a Pareto fit when the value is
The maximum likelihood method implemented by the direct optimization of the log-likelihood is given by
estim.method = "MLE". The numerical algorithm to search the optimum is the “Nelder-Mead” method implemented in the
optim function, considering as initial values those given in the
start argument or, if it is missing, those provided by the OLS method.
A different approximation of the maximum likelihood estimates is given by
estim method = "iMLE"; it is an iterative methodology where
optimize() function provides the optimum scale parameter value, while the
uniroot() function solve normal equations for that given scale parameter.
The regression estimates (
"OLS") searchs an optimum scale value (in a OLS criterion) by the
optimize() function. Then the rest of the parameters are estimated also by OLS, as appears in Sarabia and Prieto (2009).
In the L-moments method (
"LMOM") estimates are obtained searching parameters that equal the first three sample and theoretical L-moments by means of the “Nelder-Mead” algorithm implemented in
optim(); the initial values are given in the
start argument or, if it is missing, provided by the
PPSfit Object, a list with
loglikthe log-likelihood value.
nthe number of observations.
obsNamethe name of the variable with the observations.
estim.methodthe method of parameter estimation.
When this last value is
"LMOM" the function also returns details about the convergence of the numerical method involved (
Sarabia, J.M and Prieto, F. (2009). The Pareto-positive stable distribution: A new descriptive model for city size data, Physica A: Statistical Mechanics and its Applications, 388(19), 4179-4191. Hosking, J. R. M. (1990). L-moments: analysis and estimation of distributions using linear combinations of order statistics. Journal of the Royal Statistical Society, Series B, 52, 105-124.
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