Fit FMKL generalised lambda distribution to data set using quantile matching

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Description

This function fits FMKL generalised lambda distribution to data set using quantile matching

Usage

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fun.RMFMKL.qs(data, fmkl.init = c(-0.25, 1.5), leap = 3, FUN = "runif.sobol", 
trial.n = 100, len = 1000, type = 7, no = 10000)

Arguments

data

Dataset to be fitted

fmkl.init

Initial values for FMKL distribution optimization, c(-0.25,1.5) tends to work well.

leap

Scrambling (0,1,2,3) for the Sobol sequence for the distribution fit. See scrambling/leap argument for runif.sobol, runif.halton or QUnif.

FUN

A character string of either "runif.sobol" (default), "runif.halton" or "QUnif".

trial.n

Number of evenly spaced quantile ranging from 0 to 1 to be used in the checking phase, to find the best set of initial values for optimisation, this is intended to be lower than len to speed up the fitting algorithm. Default is 100.

len

Number of evenly spaced quantile ranging from 0 to 1 to be used, default is 1000

type

Type of quantile to be used, default is 7, see quantile

no

Number of initial random values to find the best initial values for optimisation.

Details

This function provides quantile matching fitting scheme for FMKL GLD. Note this function can fail if there are no defined percentiles from the data set or if the initial values do not lead to a valid FMKL generalised lambda distribution.

Value

A vector representing four parametefmkl of the FMKL generalised lambda distribution.

Author(s)

Steve Su

References

Su (2008). Fitting GLD to data via quantile matching method. (Book chapter to appear)

See Also

fun.RMFMKL.ml, fun.RMFMKL.lm, fun.RMFMKL.mm, fun.data.fit.ml fun.data.fit.lm, fun.data.fit.qs, fun.data.fit.mm

Examples

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## Fitting the normal distribution
# fun.RMFMKL.qs(data=rnorm(1000,2,3),fmkl.init=c(-0.25,1.5),leap=3)

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