GoF: Goodness of fit tests for the Pareto Positive Stable (PPS)...

In ParetoPosStable: Computing, Fitting and Validating the PPS Distribution

Description

Kolmogorov-Smirnov, Anderson-Darling and PPS goodness of fit tests to validate a PPS fit (typically from PPS.fit()).

Usage

GoF(PPSfit, k = 2000, parallel = TRUE, ncores = 2, ...)

Arguments

PPSfit	A PPSfit Object.
k	The number of iterations in the bootstrap procedure to approximate the p-values.
parallel	A logical argument specifying if parallelization is allowed in the bootstrap iteration procedure.
ncores	is the number of cores that we use if parallel is TRUE.
...	Other arguments.

Details

It returns the Kolmogorov-Smirnov, the Anderson-Darling tests and a specific test for PPS distributions. p-values are approximated by a bootstrap procedure. The specific goodness of fit test for PPS distributions is based on the linearity of the survival function vs. the scaled observations in a double log-log scale (see Sarabia and Prieto, 2009).

Value

A list with the values of the tests statistics and the approximated p-values.

References

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.

See Also

PPS.fit, plot.PPSfit

Examples

x <- rPPS(50, 1.2, 100, 2.3)
fit <- PPS.fit(x)
GoF(fit, k = 50)

ParetoPosStable documentation built on May 2, 2019, 4:05 p.m.